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Review
Determination of aroma compound partition coefficients in aqueous, polysaccharide and dairy matrices using the Phase Ratio Variation (PRV) method: a review and modeling approach Andrej Heilig, Alina Sonne, Peter Schieberle, and Jorg Hinrichs J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.6b01482 • Publication Date (Web): 16 May 2016 Downloaded from http://pubs.acs.org on May 17, 2016
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Journal of Agricultural and Food Chemistry
Title: Determination of aroma compound partition coefficients in aqueous, polysaccharide and dairy matrices using the Phase Ratio Variation (PRV) method: a review and modeling approach
Andrej Heilig†, Alina Sonne†* Peter Schieberle‡, Jörg Hinrichs†
Affiliations: †
University of Hohenheim, Institute of Food Science and Biotechnology, Department
of Soft Matter Science and Dairy Technology, Garbenstrasse 21, 70599 Stuttgart, Germany ‡
Technical University of Munich, Department of Chemistry, Lise-Meitner-Strasse 34,
85354 Freising, Germany
Corresponding author: *Alina Sonne Tel.: +49 711 459 23616; fax: +49 711 459 23617. E-mail:
[email protected] ACS Paragon Plus Environment
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Abstract
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The partition of aroma compounds between a matrix and a gas phase describes the
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individual compounds specific affinity towards the matrix constituents affecting
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orthonasal sensory perception. The static headspace phase ratio variation (PRV)
5
method has been increasingly applied by various authors to determine the
6
equilibrium partition coefficient K in aqueous, polysaccharide and dairy matrices.
7
However, reported partition coefficients are difficult to relate and compare due to
8
different
9
composition, equilibration temperature. As due to its specific advantages, the PRV
10
method is supposed to find more frequent application in the future, this review aimed
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to summarize, evaluate, compare and relate the currently available data on PRV
12
determined partition coefficients. This process was designed to specify the potentials
13
and the limitations, as well as the consistency of the PRV method, and to identify
14
open fields of research in aroma compound partitioning in food-related, especially
15
dairy matrices.
experimental
conditions,
e.g.
aroma
compound
selection,
matrix
16 17
Keywords: dairy matrix, aroma-matrix interaction, hydrophobicity, log P, multiple
18
regression
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Journal of Agricultural and Food Chemistry
Introduction
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Distribution of aroma compounds in the headspace above food-related
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matrices under equilibrium conditions has been intensively studied in the past ten
22
years. Aroma concentration in the headspace enables an estimation of the
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prospective nasal and retronasal sensory perception during the consumption of food
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matrices.1-3 Several approaches exist towards the determination of aroma compound
25
partition under equilibrium, and those most frequently used can be generally divided
26
into dynamic and static headspace analysis methods. Techniques of dynamic
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headspace analysis, also known as “purge & trap” methodology, accumulate the
28
analyte and hence offer high sensitivity, but require complex instrumentation that is
29
susceptible to faults. Static headspace techniques utilize only a small fraction of the
30
analyte and are therefore less sensitive, but very robust and easy to automate.
31
Independent of the methodology used, some techniques require aroma compound
32
specific calibration and are referred to as “direct” techniques, while others are
33
calibration independent and are hence called “indirect” techniques. Aroma compound
34
partition in aqueous, polysaccharide and dairy matrices has been investigated in
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numerous studies using the above techniques. However, the results obtained by
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various dynamic and static headspace analysis show a considerable lack of
37
agreement.4,5 A review on the headspace analysis of aroma compounds has been
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given by Biniecka & Caroli.6
39
Of late, the phase ratio variation (PRV) method, an indirect static headspace
40
technique, has become increasingly popular to determine the aroma compound
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partition coefficient K.7-12 The PRV method has been explained in detail by Ettre,
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Welter & Kolb7, who showed that within certain boundaries, K can be ascribed to the
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relation of peak area A and phase ratio β.
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1 c ME 1 = − A cGE f ⋅ c0
⋅ β ⋅ f ⋅ c 0
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K MG =
45
where KMG is the matrix/gas partition coefficient, cME is the concentration of the
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analyte in the sample matrix phase, and cGE is the concentration of the analyte in the
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sample gas phase, i.e. the headspace of a closed vial under equilibrium conditions. A
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is the chromatographic peak area, f is a proportional factor, c0 is the initial aroma
49
compound concentration in the matrix and β is the phase ratio of headspace volume
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VGE and matrix volume VME in the vial under equilibrium conditions.
(1)
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Rearranging Eq. (1) according to the experimentally assessable terms 1/A and
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β results in a linear relationship with (KMG/ (f*c0)) as the intercept and (1/(f*c0)) as the
53
slope.
54 55
1 = K MG A f ⋅c 0
1 + ⋅ β f ⋅ c0
(2)
56 57
KMG is then calculated from the ratio of the intercept and the slope. Inversion of
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the matrix/gas partition coefficient yields the gas/matrix partition coefficient KGM,
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which many studies report instead of the KMG.
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K GM =
1 K MG
(3)
62 63
The main advantages of the PRV method are the calibration-free approach
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to K-value measurement, easy sample preparation, the high degree of analysis
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automation, the possibility to measure the partition coefficients of numerous
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compounds added together in the matrix during the same experiment (aroma
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compounds with distinct Kovats retention indexes), and good reproducibility. After
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comparison with other well-established static headspace techniques, Athes et al.4
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concluded that the PRV method offers the best compromise in terms of accuracy,
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reliability, and simplicity.
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Downsides of the PRV-method include restrictions in the determination of
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very large (> 102) and very small (< 10-2) K-values, which originates from the
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insufficient change in chromatographic peak area in both cases.13 In the case of
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highly viscous matrices, the transfer of exact volume of matrix can be challenging.
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However if the density is known, such matrices can be weighed instead as well.
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Furthermore, single K-values from linear regression are statistically less reliable
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than from non-linear regression, i.e. from the reciprocal function 1/A. This results in
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tighter and more reliable confidence intervals.14
79
Past studies dealing with PRV-based K-value determination have used a
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wide variety of aroma compounds with different physico-chemical properties to
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determine the partition coefficient in water, as well as in polysaccharide and dairy
82
matrices of different composition, and various data processing methods have been
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used. Despite its frequent application, a quantitative comparison of PRV-results
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obtained by different researches is not easy to perform, as matrix composition is
85
not defined and expressed in a uniform way, and partition coefficient analysis
86
temperature varies. This complicates the use of past results and the identification
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of main parameters, potential correlations, as well as existing knowledge gaps in
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the PRV analysis of partition coefficients.
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In this context, it was the aim of this review to establish a K-value database
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that is relevant to dairy matrices and to identify future areas of research. Although
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this review is primarily concerned with the results of PRV-determined aroma
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compound partition, studies that have used other methods of analysis will be ACS Paragon Plus Environment
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mentioned, where appropriate. Statistic analysis was performed to derive
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equations that allow for the calculation of K-values depending on aroma
95
compound and dairy matrix properties, as well as analytical conditions.
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Materials and methods
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Preparation and analysis of flavored model dairy matrices. Model dairy matrix
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production and KMG-value determination was performed according to Heilig et al.15,16
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Preparation of flavored model dairy matrices. Model milk solutions were
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composed of micellar casein powder (in-house production) and whey protein isolate
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powder (DSE 5627, Fonterra co-operative Group Ltd., Auckland, New Zealand)
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dissolved in reconstituted ultrafiltration permeate (Bayolan PT, BMI, Landshut,
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Germany). In-house produced cream and amidated low-methoxy pectin (CU-L
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082/07, Herbstreith & Fox, Neuenbürg, Germany) were added for the production of
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fat and hydrocolloid containing matrices. All solutions were batch pasteurized at 65
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°C with a holding time of 30 min. Subsequently, the solutions were cooled down to 10
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°C in ice water and then stored overnight at 4 °C. Resulting model milk matrices were
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then flavored with 1.0 % (w/w) of a commercial strawberry aroma from Symrise AG
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(Holzminden, Germany). Limonene and diacetyl, obtained from Symrise AG, were
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added to extend the log P range of the aroma compounds under investigation. Table
112
1 lists the final composition of the propylene glycol based aroma. The pH of the final
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flavored model milk matrices was 6.75 ± 0.05.
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Model yoghurt systems were produced according to Krzeminski et al.17 Briefly,
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raw milk was separated, pasteurized (74 °C, 30 s), adjusted in protein content by
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using low heat skim milk powder (type Instant C, Schwarzwaldmilch GmbH,
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Offenburg, Germany) and fat content by using cream (in-house production),
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homogenized (65 °C, 15/3 MPa), heated in a tubular heat exchanger (95 °C, 5 min),
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cooled to 35 °C, fermented either with glucono-δ-lactone GDL (Art.-No. 49210, CAS
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604-69-3, Sigma-Aldrich, Munich, Germany) or with FD-DVS Yo-Flex® 812 (Chr.
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Hansen GmbH, Nienburg, Germany) at 35 °C to a pH of 4.4 – 4.2. Resulting set
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yoghurt gel was manually stirred while adding 1.0 % (w/w) of a commercial
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strawberry aroma from Symrise AG (Table 1).
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The degree of whey protein denaturation (DWPD) was determined by RP-
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HPLC.18 Own KMG-values, which have not been published by the authors in previous
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papers, are marked as such by an asterisk in Table 2.
127 128
Determination of the matrix/gas partition coefficient KMG. Vials containing
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flavored model milk matrices were equilibrated for 15 min at 40 °C in an automatic
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headspace sampler QHSS40 (QUMA Elektronik & Analytik GmbH, Wuppertal,
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Germany). The vials were gently agitated using the QHSS40 integrated shaker. After
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equilibration, 1 mL of vial headspace was automatically withdrawn at a valve
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temperature of 90 °C and a tube temperature of 150 °C. Headspace analysis was
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performed on a CP-3800 gas chromatograph (Varian Deutschland GmbH,
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Darmstadt, Germany), equipped with a split/splitless injector CP-1177 (240 °C) and a
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flame ionisation detector (250 °C, H2 28 mL min-1, synthetic air 300 mL min-1, N2 30
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mL min-1). For samples with 2 % fat, a split-ratio of 1:50 was used, and 1:20 for the
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higher fat contents. An HP-FFAP capillary column with an inner diameter of 0.32 mm,
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a film thickness of 0.25 µm and a length of 30 m (Agilent Technologies, Waldbronn,
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Germany), was used for chromatographic analysis. A deactivated silica-coated with
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an inner diameter of 0.53 µm and a length of 5 m served as pre-column. The carrier
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gas was H2 (2 mL min-1). The oven program started at 40 °C for 5 min, followed by
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heating up with 5 °C min-1 to 100 °C and 40 °C min-1 to 240 °C with a holding time of
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5 min.
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Vials containing model yoghurt systems were equilibrated at 40 °C for 30 min,
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and split-ratios of 1:50 and 1:20 were used for 0 % and 4 % fat, respectively. The
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determination of the matrix/gas partition coefficient KMG was performed by means of
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the phase ratio variation (PRV) method as described by Ettre, Welter, & Kolb7.
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Different volumes (50, 75, 100, 150, 200, 500, 1000, 2000 µL) of the flavored model
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dairy matrix were transferred into headspace vials of 22 mL (QUMA Elektronik &
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Analytik GmbH, Wuppertal, Germany). After filling, the vials were immediately sealed
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with PTFE septa in metallic caps (QUMA Elektronik & Analytik GmbH, Wuppertal,
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Germany).
154 155
Transformation of dairy matrix and partition coefficient data from the
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literature. If not given as ppm (w/w) in the literature, the aroma concentration present ACS Paragon Plus Environment
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in the final product was calculated either from the molecular weight (if added as
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mol/L) or the specific gravity ρs (if added as µL/L) of the respective aroma compound
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(Table 2).
160
Where the casein protein to whey protein ratio (CWR), the degree of whey
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protein denaturation (DWPD) and the lactose content in the analyzed dairy matrices
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have not been given explicitly, the following assumptions were made:
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For dairy matrices made (or reconstituted) from skim milk (powder) CWR =
164
80:20, lactose content = protein content * 1.37 and ash content = protein content *
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0.21.19 Ash content in dairy matrices made from ultrafiltrated skim milk retentate was
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calculated as protein content * 0.11.20 For dairy matrices made from whole milk CWR
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= 80:20, lactose content = protein content * 1.43 and ash content = protein content *
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0.22. For skim and whole milk, protein contents of 3.50 and 3.33 were assumed,
169
respectively. The DWPD was estimated according to Kessler19 from the time-
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temperature data of the (reconstituted) milk treatment given by the authors.
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In cases where the gas/matrix partition coefficient KGM instead of the KMGvalue was reported, KMG was obtained by simply inverting KGM according to Eq. (3).
173 174
Statistics. The influence of aroma compound properties, dairy matrix
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composition and analytical conditions on the matrix/gas partition coefficient KMG was
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assessed by means of multiple regression using Statgraphics Plus Version 5.1
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(Statpoint Technologies Inc., Warrenton, USA).
178 179
Results and discussion
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Review of literature data. Table 2 summarizes 375 experimental K-values
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that were published in 15 scientific papers, as well as 167 additional K-values
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determined by the review authors. K-values from the studies of other authors were ACS Paragon Plus Environment
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either taken from the depicted tables or extracted from figures if possible. 56 different
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aroma compounds have been investigated by means of the PRV method. They
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comprise a wide variety of chemical classes, including several aldehydes, ketones,
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carboxylic acids, esters, alcohols and terpenoids. In total, 542 data sets describe the
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aroma compounds partition between 90 different matrices and air at thirteen different
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equilibration temperatures that range from 4 to 80 °C. The reported 542 KMG-values
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range from as little as 2.67 to as much as 3.31 * 104 (Table 2, lines 1 and 52,
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respectively).
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Apart from pure water (95 data sets) and aqueous solutions or gels of
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polysaccharides (125 data sets), 40 differently composed dairy matrices with protein
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contents of 3.2 to 12.0 % (305 data sets on 11 levels), fat contents of 0.1 to 14.8 %
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(105 data sets on 9 levels), a CWR of 0.0 to 100 (305 data sets on 11 levels),
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thickener concentrations of 0.05 to 4.01 % (43 data sets on 3 levels), disaccharide
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concentrations of 4.3 to 12.5 % (305 data sets on 23 levels), ash contents of 0.49 to
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4.11 % (305 data sets on 25 levels), a DWPD of 2.7 to 99 % (303 data sets on 8
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levels) and pH values of 4.0 to 6.8 (305 data sets on 6 levels) have been analyzed
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for their aroma retentive capacity at aroma compound concentrations (ACC) from 1 to
200
1000 ppm (w/w).
201
Next to the investigated aroma compounds log P-value and vapor pressure,
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which are measurands of hydrophobicity and volatility, most studies list further aroma
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compound specific physico-chemical properties, such as molecular weight, boiling
204
point, melting point, water solubility, specific gravity etc. Mostly, this is done without
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any apparent reason, as the named parameters are seldom used to explain the
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obtained analytic results. If the intention is to adequately specify the investigated
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compounds, which is highly recommended given the confusing multitude of
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sometimes conflicting trivial names that exist for one and the same compound, such ACS Paragon Plus Environment
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a specification should include the Chemical Abstracts Service (CAS) number. The
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CAS-No. is highly specific and even differentiates between the enantiomers of a
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compound.
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In Table 2 the CAS-No. is given together with the compounds molecular
213
structure, as well as its log P-value, molecular mass (M), boiling point (BP) and
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saturated vapor pressure (pi0). These parameters were preferred over others,
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because they are considered to contain the most valuable information regarding the
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aroma compounds partition behavior and detectability, as it will be discussed later. If
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the reviewed studies did not list the CAS-No., the most likely number was selected
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after taking into account additional information and parameters that were provided by
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the respective authors.
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Regarding the above named physico-chemical parameters, the investigated
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compounds span log P values of -1.48 to +4.51 (Fig. 1 (A)), molecular masses of 41
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to 172, boiling points of -2 to 286 °C and vapor pressures of 0.00 to 1.20 * 103 hPa.
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In Table 2, the aroma compounds have been arranged in descending order
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according to their log P mean value. The log P mean value was calculated from the
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mostly inconsistent log P-values that are reported by various studies and databases.
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This was done because log P is an important parameter with regard to the retention
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of aroma compounds. The log P-value is seldom determined experimentally
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according to OECD guidelines21, but rather calculated by various software programs
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that use different QSPR-assumptions based on the methods of Rekker22 or Hansch &
230
Leo23.
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Fig. 1 (B) displays the magnitude distribution of the 542 KMG-values listed in
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Table 2. As it is indicated by the dashed lines, 95 % of the values lie in between 1.01
233
* 101 and 3.85 * 103, 90 % lie in between 1.55 * 101 and 2.33 * 103, and 75 % are
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located in between 2.56 * 101 and 8.89 * 102 (bordered by the outer, intermediate ACS Paragon Plus Environment
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and inner dashed lines, respectively). The fact that the outer 5 % of the partition
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coefficients comprise, in comparison to the majority of values, very low and very high
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KMG-values, will be a matter of discussion in the following sections.
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While log P influences the magnitude of the KMG-value in aqueous and fat
239
containing matrices, it is rather unimportant with regard to compound detectability.
240
The latter depends more on BP and pi0, which becomes obvious when summarizing
241
the aroma compounds that could not be detected via static-HS-GC using a flame
242
ionization detector (Table 3).
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When these mostly non-detectable compounds are compared with the ones
244
listed in Table 2, their high boiling point and / or low saturated vapor pressure
245
becomes obvious. However, KMG-values from ten of the 20 compounds listed in
246
Table 3 have been reported by other authors. In the case of δ-decalactone, methyl
247
cinnamate and vanillin, KMG-values are only given by Atlan et al.14 in water, while
248
KMG-values for benzyl acetate and 3-methylbutanal have only been reported by
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Martuscelli et al.24 and Bylaite et al.25, respectively. The non-detectability of ethyl
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octanoate, linalool, octanal and diacetyl is most likely to be a combined result of low
251
concentration, partly coupled with low equilibration temperature and highly aroma
252
retentive matrices in the respective studies.
253
When examining the physico-chemical properties of the aroma compounds
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listed in Table 2 and linking that information to the non-detectable compounds in
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Table 3, it appears that aroma compounds with a boiling point >> 200 °C at 1013
256
hPa and / or a saturated vapor pressure < 0.1 hPa at 25 °C are hard to detect via
257
static-HS-GC using an FID-detector, for infinitely dilute solutions.7
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Regarding aroma compounds with elsewhere reported KMG-values, this would
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include decanoic acid, δ-decalactone, methyl cinnamate and vanillin. With these
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in water (3.31 * 104 and 3.19 * 103, Table 2, lines 52 and 439, respectively), as
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reported by Atlan et al.14, appear extraordinary, especially when taking into account
263
the aroma compounds log P mean values of +4.09 and +1.20. The high KMG-values
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are most likely the result of the compounds low volatility (Table 3), not the one of
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actual retention in water.
266 267
Modeling of KMG from literature data.
268
Selection of model parameters. It is neither the obligation nor the aim of this
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review to derive the best possible or most plausible fit to the available data. Data
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fitting is neither a science nor a uniform process, and many goal-dependent
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approaches exist towards the multiple regression of large data sets.26 However, it is
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of great practical interest to determine to which degree the partition of aroma
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compounds can be forecasted from the available data. A potential application is the
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reformulation of aroma compositions in the dairy product development process.
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Accompanied by statistical analysis, a fitting attempt can furthermore help to extract
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experimental areas that are worth of future investigation, as well as questionable data
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sets, i.e. outliers in highly heterogeneous data.
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While Table 2 makes for an impressive set of experimentally determined KMG-
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values, which covers an exceptionally wide range of aroma compounds and
280
differently composed matrices as well as analytic conditions, the multiple regression
281
of these values is complicated to perform.
282
First, the data to be fit must be defined. When plotting the listed log P-values
283
according to their magnitude and frequency (Fig. 1 (A)), it becomes obvious that at
284
both ends of the magnitude spectrum, relatively few values will execute a high, and
285
probably misleading, influence on the fit.
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Second, the number of independent variables that are included in the model
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should be carefully observed and reduced to a well-considered minimum. Although
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an increased number of independent variables, i.e. fitting parameters, will always
289
lead to an improved coefficient of determination (R²) value of the fit, the inclusion of
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insignificant parameters will also increase the residual mean square and hence the
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standard error of the estimate SEE, as well as the mean absolute error MAE.26 With
292
regard to aromatized dairy matrices, a multitude of composition and process-related
293
variables, and hence potential regression parameters, exist. These will be discussed
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in the following.
295 296
Aroma compound physico-chemical properties. The included parameters
297
should exert their effect on aroma compound partition founded preferably on
298
fundamental physico-chemical interrelationships, such as the affinity for hydrophobic
299
binding. Otherwise, such parameters will only obscure the principles that lie beneath
300
the aroma compound-matrix interaction, and unnecessarily complicate the model.
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From the information summarized in Table 3, boiling point, as well as
302
saturated vapor pressure pi0, must be respected as a regression parameter,
303
especially in combination with the variety of applied equilibration temperatures listed
304
in Table 2. Molecular mass can be neglected, as it is linearly correlated with boiling
305
point BP (R2 of 0.80 for the present data set).
306
Log P is a measure of aroma compound hydrophobicity / lipophilicity and
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numerous studies have shown its influence on aroma compound partition in a vast
308
variety of matrices. While to some degree, log P is correlated with molecular mass,
309
too, this is not for fundamental reasons. Both parameters’ relationship originates from
310
the fact that a higher hydrophobicity, and hence a higher log P, greatly depends on
311
the length of the aliphatic chain that forms part of most aroma compound molecules. ACS Paragon Plus Environment
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Still, a comparably “light” compound like limonene (molecular mass 136) can be
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exceptionally hydrophobic (mean log P = +4.51) if an aromatic ring is formed. Vice
314
versa, an aroma compound with intermediate molecular mass, like diacetyl with 86,
315
can be exceptionally hydrophilic (mean log P = -1.48). In the case of diacetyl, this is
316
due to the ability of the molecules to form dimers and hydrogen bonds.27
317 318
Aroma compound concentration (ACC). The thermodynamic principles, on
319
which the determination of the partition coefficient is based, are only valid in ideal
320
diluted solutions. In general, such a condition exists when the concentration of the
321
volatile compound in the matrix does not exceed 0.1 %.13 Aroma compound
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concentrations in the reviewed literature range from 1 to 1000 ppm (w/w), i.e. 0.0001
323
to 0.1 %, which means that aroma compound concentration can be neglected as a
324
fitting parameter. In a comparative study that included several PRV operating
325
parameters, Athes et al.4 found that at aroma compound concentrations of 20 to 50
326
ppm (w/w), the compound specific K-values that were obtained at analyzing solutions
327
that contained either a single or 15 different compounds were comparable.
328
Consequently, the composition of aromatization can be neglected, too.
329 330
Dairy protein content. Protein content can be considered to be the single most
331
important factor in the design of dairy matrix texture. An excellent review on the dairy
332
protein-aroma compound interaction has been given by Kühn et al.28. Several studies
333
have proved the aroma binding potential of milk proteins. Merabtine et al.29,
334
Gierczynski et al.30 and Heilig et al.15 used the PRV method and reported an up to
335
ten-fold, aroma compound dependent increase in retention at 4 to 12 % milk protein,
336
as compared to aqueous solutions. Meynier et al.31 and Paçi-Kora et al.32 compared
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aroma retention in skim milk and water using other static HS methods, but with
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comparable results.
339
The main component of dairy protein is casein, which effect was studied by
340
Landy et al.33 and Fares et al.34 using sodium caseinate at concentrations from 0.5 to
341
7.5 % (w/v). A higher caseinate content led to an aroma compound and caseinate
342
dependent retention (diacetyle, benzaldehyde, ethyl hexanoate) or release (diacetyl,
343
2-propanol) as determined by static and dynamic methods of HS-analysis. Acetone
344
and ethyl acetate showed no change compared to water. Andriot et al.35 investigated
345
different concentrations of β-lactoglobulin (0.5, 1, 2, 3, and 4 %), the second most
346
important protein in milk, and observed a higher retention as well as a lower odor
347
intensity of methyl ketones (2-heptanone, 2-octanone and 2-nonanone) at increased
348
β-lactoglobulin contents.
349
305 KMG-values, originating from 40 protein-containing matrices, are available
350
to assess the influence of protein content on aroma compound retention via multiple
351
regression.
352 353
Milk fat content / type. The contribution of milk fat to the flavor of dairy
354
products becomes increasingly important, although more because of its gradually
355
increasing absence in fat-reduced varieties rather than its presence. Flavor research
356
regarding the aroma compound fat interaction can be roughly distinguished into
357
works dealing with either the type of fat, the fat content, or the particle size
358
distribution (PSD) properties of the fat phase, as well as combinations thereof. With
359
regard to the assessment of milk fat influence, the PRV method has been applied by
360
Martuscelli et al.24, Deleris et al.36 and Benjamin et al.37, and for hydrophobic
361
compounds such as limonene, 500 times higher KMG-values in comparison to
362
aqueous solutions have been reported between fat contents of 0.5 % and 14.8 %. ACS Paragon Plus Environment
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363
The influence of fat type on aroma partition at equilibrium has been
364
investigated by Roberts et al.38, Relkin et al.39 and Benjamin et al.37. They found that
365
more hydrophobic aroma compounds are comparably less retained at higher shares
366
of solid fat, originating either from fat origin or equilibration temperature. Kopjar et
367
al.40 used standard and high melting AMF for PRV experiments with full-fat yoghurt,
368
but unfortunately only listed aroma retention relative to fat-free yoghurt instead of
369
absolute K-values. While high-melting AMF appeared to retain approximately 20 %
370
less ethyl acetate, ethyl butanoate and ethyl hexanoate than standard AMF, no other
371
dairy related PRV study has been concerned with the share of solid, i.e. high-melting
372
fractions in milk fat.
373
Independent of the type of fat investigated, i.e. paraffin oil1, sunflower oil41, soy
374
oil3,37, rapeseed oil2,42 ,coconut oil38, hydrogenated palm kernel oil39, hydrogenated
375
palm fat38, MCT-oil38,43, AMF31,36,37,39 or cream fat31,38,44, less hydrophobic aroma
376
compounds were either not affected or less retained. This means that the dairy
377
matrices were equally or more odorant at higher fat contents. Conversely, more
378
hydrophobic compounds had a higher retention (Table 4). The extent of increased
379
retention was found to be positively correlated with the aroma compounds
380
hydrophobicity as expressed by their log P-value.
381
Conflicting results are reported with regard to the particle size distribution of
382
the fat phase. Many factors such as fat type, fat content, aroma compound
383
hydrophobicity and equilibration temperature have been argued to explain this
384
variation.2,41,43
385
Table 2 lists 105 KMG-values that were determined in 26 fat-containing
386
matrices, and due to its well-known effect on aroma compound partition, fat content
387
was included as a fitting parameter. Due to the insufficient data base that is available
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388
on the influence of solid fat share and PSD, these two fat-associated parameters
389
were neglected.
390 391
Casein to whey protein ratio (CWR). With membrane filtration fractionation
392
techniques becoming the standard in dairy production processes, the CWR is of
393
increasing practical importance. Different ratios of casein protein to whey protein
394
have been investigated via the PRV method concerning the aroma partition above
395
milk protein solutions15 and stirred yoghurts.45,46
396
An increased share of micellar casein protein did not affect the partition of
397
diacetyl and ethyl hexanoate in solutions of 4 % protein, but caused a higher
398
retention of limonene, which was attributed to the hydrophobic core of the casein
399
micelle.15
400
In stirred yoghurt, most of the aroma compounds investigated by Saint-Eve et al.45
401
and Deleris et al.46 were more retained by addition of sodium caseinate than by whey
402
protein concentrate.
403
As already mentioned, a comprehensive review on the interaction of dairy
404
proteins and aroma compounds has been published by Kühn et al.28 Non-covalent as
405
well as covalent binding mechanisms exist, and their respective prevalence and
406
extent depends on the aroma compound properties, the protein functionality and the
407
composition of the aqueous environment. Given the variety in which both caseins and
408
whey proteins appear, such as native casein micelles, different caseinate salts, whey
409
protein isolates and concentrates etc., there is no such thing as “casein” and “whey
410
protein”. The often conflicting results regarding the aroma retention potential of both
411
fractions are likely to originate from these differences, which should be specified
412
accordingly. However, CWR has the potential to influence aroma partition, and 305
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413
data sets on 11 levels are available. Therefore, CWR was assessed as specified in
414
Table 2 and included as a fitting parameter.
415 416
Thickener content / type. Thickeners are routinely applied in a multitude of
417
dairy products, and especially in fat-reduced varieties, where they are intended to
418
contribute to texture formation. The effect of thickeners, i.e. polysaccharides such as
419
agar, carrageenan, cellulose, guar, locust bean, maltodextrin, pectin, starch, xanthan,
420
as well as their varieties, on aroma compound retention has been extensively studied
421
in protein-free, aqueous systems, as well as in dairy matrices. Studies mostly varied
422
the thickener concentration, and sometimes the salt content at constant contents of
423
ionic thickeners. In both cases not only the composition, but also the texture of the
424
aqueous solution changes.
425
PRV primary data are available from Bylaite et al.25, Savary et al.47, Deleris et
426
al.48, Lauverjat et al.5 and Merabtine et al.29 for proteinfree aqueous systems and
427
from Merabtine et al.23 for stirred yoghurt. In aqueous systems, it appears that as a
428
rule of thumb, retention sets in once the thickener concentration is high enough to
429
translate the viscous solution into a gel network49,often supported by the addition of
430
sucrose50, and that the extent of retention is positively correlated with aroma
431
compound hydrophobicity.51-53
432
Concerning dairy matrices it was found that depending on the concentration
433
and the presence of sucrose, thickeners can either increase or decrease the aroma
434
compound retention in stirred fat-free, but not in full-fat yoghurt54,55. A “salting-out”
435
effect has also been proposed.40,55 No thickener effect was seen in the non-acidified,
436
sucrose containing yoghurt base32, nor in fat-free and full-fat dairy custards.56
437
The general role of thickeners in the partition of aroma compounds is nearly
438
impossible to assess with reference to the literature, as the physico-chemical ACS Paragon Plus Environment
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439
properties of the investigated thickeners vary greatly from study to study, even within
440
one thickener type (i.e. high-methoxy, low-methoxy, amidated low-methoxy pectin),
441
and a multitude of additional matrix constituents complicate the comparison of
442
results. Regarding the various individual thickeners reported in Table 2, most of them
443
have not been sufficiently varied to respect them as single parameters in multiple
444
regression. The PRV-database contains 168 thickener-associated K-values on 12
445
levels of thickener content, and all water-binding polysaccharides were summarized
446
as thickeners. In order to assess whether and to which degree their addition results in
447
a directed change of the partition coefficient or not, thickener content was included as
448
a fitting parameter.
449
Disaccharide content. As it has already been stated, various studies contribute
450
a considerable effect on aroma partition to the presence of sucrose in thickened
451
aqueous systems. A 25 to 75 % decrease of ethyl acetate, ethyl hexanoate and n-
452
hexanol solubility in sucrose and maltodextrin solutions (30.0 to 57.5 %) as compared
453
to water has been reported by Covarrubias-Cervantes et al.57. A variation of the
454
sucrose content between 2.5 and 10 % in dairy custards had no effect on the
455
partition of various esters and aldehydes.58
456
Independent of additional sucrose, changes in the composition of a dairy
457
matrix, especially with regard to the protein and fat content, will always lead to a shift
458
in the concentration of disaccharides because of the inherent lactose, which has also
459
been suspected to cause a “salting-out” of ester aroma compounds.32 This shift
460
affects not only the overall disaccharide concentration, but even more so the
461
concentration in the aqueous phase. Because of the potential “salting-out” effect, the
462
disaccharide content should be included in modeling the matrix-aroma interaction.
463
Accordingly, the disaccharide content in PRV investigated matrices has been
464
calculated with reference to established protein to lactose coefficients. As a result, ACS Paragon Plus Environment
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465
Table 2 includes 423 KMG-values associated with disaccharide containing matrices
466
on 27 levels of disaccharide content.
467 468
Ash content. The addition of salts to aqueous solutions of aroma compounds
469
increases the compounds headspace concentration due to a “salting-out” effect.
470
Although this “salting-out” effect of salt on aroma compounds release is mainly
471
observed in aqueous solutions, it is also known to vary with compound
472
characteristics59, and has also been reported for β-lactoglobulin solutions.60 However,
473
in more complex matrices, salt can also be responsive of aroma compounds
474
retention since it induces proteins or polysaccharides structure modifications. As it is
475
the case with disaccharides, the salt concentration will inevitably change upon
476
modifications in the concentration of original dairy constituents, which should be
477
respected accordingly. Dairy matrix inherent salt, i.e. ash content, was therefore
478
calculated using the protein to ash ratios specified in Table 2, and there are 411 KMG-
479
values available to assess the potential “salting-out” effect of ash content on aroma
480
compound retention.
481 482
Degree of whey protein denaturation (DWPD). The kinetics of thermally
483
induced whey protein denaturation have been the subject of intense investigation and
484
were summarized for example by Kessler19. In dairy technology, the DWPD decides
485
over the gel forming properties during the manufacture of fermented milk products
486
such as yoghurt and cheese.20 It is therefore important to notice the results of
487
numerous studies, which have focused on the structure-property relationship with
488
regard to aroma compound binding by β-lactoglobulin.28 β-lactoglobulin is the by
489
quantity major dairy whey protein and its tertiary structure is lost upon heating, which
490
can result in increased or decreased binding, depending on the aroma compound ACS Paragon Plus Environment
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491
and the degree of denaturation.28 Perreault et al.61 used static HS-GC to determine
492
the aroma concentration above non-heated and 90 °C / 5 min heated skim milk as
493
well as acid gels made from it and reported an aroma compound dependent
494
influence of heating. It is important to note that acidified dairy matrices possess less
495
hydrophobic potential if made from milk with a high DWPD, as our own results have
496
shown.62 This could influence the retention of hydrophobic aroma compounds, and
497
with 303 available data sets on 8 levels, DWPD was included as a regression
498
parameter.
499 500
pH. The production of dairy products is often accompanied by a change
501
towards lower pH. Heilig et al.15 used PRV methodology to describe the effect of pH-
502
reduction, i.e. acidification, on aroma partition in fat-free dairy matrices. Hydrophilic
503
diacetyl was less retained in acidified matrices, while hydrophobic aroma compounds
504
were rather unaffected, which is in accordance with the results of Paçi-Kora et al.32,
505
Leksrisompong et al.3 and Perreault et al.61
506
Heilig et al.15 attributed this behavior to increased matrix hydrophobicity at acid
507
pH. In aqueous solutions of β-lactoglobulin, acid pH was also shown to decrease
508
aroma compound retention, due to changes in β-lactoglobulin tertiary structure.63,64
509
Given the potential synergistic or antagonistic effects with DWPD via the hydrophobic
510
binding of proteins, pH (542 available data sets on 8 levels) was included as a fitting
511
parameter.
512 513
Equilibration temperature (ϑ). The saturated vapor pressure, and hence the
514
distribution of an aroma compound between two media, is temperature-dependent.
515
Saturated vapor pressure at different temperatures can be calculated according to
516
the Antoine equation. Using such conversions, Kopjar et al.40 reviewed the effect of ACS Paragon Plus Environment
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Journal of Agricultural and Food Chemistry
Page 22 of 66
517
temperature on aroma partition in dairy relevant food matrices. The temperature-
518
dependence of aroma compound K-values, determined via the PRV method, has
519
been investigated in aqueous matrices with and without added thickeners within a
520
temperature range of 10-80 °C.47,52 As expected, Arrhenius-plots showed a linear
521
dependency of ln K vs. 1/T.
522
Meynier et al.31, Roberts et al.38 and Nongonierma et al.54 used static-HS
523
methods to determine the aroma partition in fat-free and fat-containing dairy matrices
524
between 4 and 80 °C. The temperature-dependent change in retention was more
525
pronounced in fat-containing matrices than in fat-free matrices attributed to the share
526
of solid fat at a given temperature. The retention was lower at higher temperatures
527
and the change in fat-containing matrices was larger. This is because the proportion
528
of solid fat decreases at higher temperatures and the retention of aroma compounds
529
decreases.
530
As equilibration temperature not only governs the aroma compounds vapor
531
pressure, but also influences the solid to liquid ratio of milk fat, it is an essential
532
parameter in the assessment of aroma partition. The thirteen equilibration
533
temperatures summarized in Table 2 vary from as little as 4 °C to as much as 80 °C.
534 535
Texture. Dairy matrices do not only differ in composition, but due to process
536
technology operations, in texture, too. Texture is known to greatly influence aroma
537
perception during the consumption of dairy products.31 PRV studies that considered
538
the effect of texture on aroma compound equilibrium partition include those of Bylaite
539
et al.25, Saint-Eve et al.45, Gierczynski et al.30 and Deleris et al.46 In pectin thickened
540
aqueous solutions, there was no influence of viscosity on levels up to 1000 times the
541
viscosity of water.25 Gierczynski et al.30 found no influence of model fresh-cheese gel
542
hardness on aroma partition at equilibrium, and similar observations were made by ACS Paragon Plus Environment
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Journal of Agricultural and Food Chemistry
543
Saint-Eve et al.45 and Deleris et al.46 regarding the viscosity of full-fat stirred
544
yoghurts.
545
measured at the thermodynamic equilibrium and reflect the affinity of aroma
546
compounds with the matrix i.e. interactions with matrix ingredients. Thus, it was
547
expected that texture affects the kinetic of the release in the headspace and could be
548
evaluated through diffusion or transfer coefficients. One should distinguish two
549
phenomena: on the one hand, the binding of aroma compounds with pectin (effect at
550
equilibrium) and on the other hand, the increase in viscosity inducing a decrease in
551
the time to reach equilibrium. However, because most food, and especially dairy
552
matrices, show non-newtonian, shear thinning behavior, it is not possible to
553
determine absolute viscosities. The methodology dependent apparent viscosities
554
given in the literature are by no means comparable and cannot be used for
555
regression purposes. Therefore, texture parameters were neglected for the modeling
556
of K-values.
It is generally assumed in the literature that partition coefficients are
557
The above summarized information shows that a considerable effect on aroma
558
compound partition can be expected from the log P, BP and pi0 on the aroma
559
compound side, the protein, fat, thickener, disaccharide and ash content, as well as
560
CWR, DWPD and pH on the matrix side, and equilibration temperature on the
561
analytical side. All these parameters have been sufficiently varied and interaction
562
effects between some of them are likely. The direction and extent to which these
563
parameters influence aroma partition depends on the aroma compounds and dairy
564
constituents physico-chemical properties. Therefore, they were chosen as fitting
565
parameters.
566 567
Modeling all matrices. An undifferentiated modeling approach, which only
568
excludes the anomalous KMG-values reported for decanoic acid and vanillin (Table 2, ACS Paragon Plus Environment
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Journal of Agricultural and Food Chemistry
Page 24 of 66
569
lines 52 and 439, respectively) for the reasons explained above, results in a
570
regression model with an R2 of 0.44 (SEE = 1078, MAE = 579, i = 540). As it can be
571
seen from the plot of observed versus predicted KMG-values (Fig. 2 (A)), only a
572
handful of KMG values > 5000 exerts a disproportional influence on the fit. Excluding
573
the smallest and the largest 2.5 % of the KMG-values listed in Table 2 improves the fit
574
to an R2 of 0.53 (SEE = 466, MAE = 306, i = 512; Fig 2 (B)).
575
Both diacetyl and limonene15,16 were shown to demonstrate anomalous
576
partition behavior in dairy matrices. After excluding these two by far most hydrophilic
577
and hydrophobic aroma compounds (Fig. 1 (A)) from the model, and excluding the
578
smallest and the largest 2.5 % of the remaining 433 KMG-values, an R2 of 0.69 is
579
obtained (SEE = 263, MAE = 167, i = 411; Fig. 3). However, the partition of both
580
diacetyl and limonene will be discussed separately further below (see Chapter
581
Modeling selected aroma compounds).
582
As it has been stated initially, it is not the aim of this review to establish the
583
best probable fit. With respect to the available data base, and the wide range of
584
experimental conditions covered, the fitting attempts presented here intend to
585
investigate to which degree a satisfactory forecast of PRV-determined partition
586
coefficients is possible, and if the K-values that were reported by numerous
587
researchers are coherent.
588 589
Modeling selected matrices. The above fittings, which included all
590
constituents alike, might be considered as too comprehensive. A more specific point
591
of view could distinguish three principally different types of matrices, namely protein
592
and fat-free aqueous solutions, such as milk permeate, as well as protein-containing
593
matrices with and without milk fat. Separate processing of the partition coefficient
594
data that is available on these fundamentally different matrices can help to correctly ACS Paragon Plus Environment
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Journal of Agricultural and Food Chemistry
595
assess the influence of the compositional parameters that define them. In Table 2,
596
the available data has already been arranged accordingly by empty lines within the
597
single aroma compounds.
598
As it is well known from previous research, some dairy constituents impose
599
more effect on aroma compound retention than others. Examination of the KMG-data
600
listed in Table 2 shows that for most of the investigated aroma compounds, three
601
levels of magnitude exist. These levels correspond to protein- and fat-free matrices,
602
protein containing but fat-free matrices, and protein- and fat-containing matrices. The
603
KMG difference between these levels is approximately one decade, i.e. increasing
604
from 1-10 to 10-100 and 100-1000 with increasing matrix complexity, as it is the case
605
for example for limonene (Table 2, lines 1-40). Changes in the constituent
606
composition of less complex matrices, which would probably have a considerable
607
influence on aroma compound retention in the latter, are likely to be underestimated if
608
KMG-data is modeled without respect to matrix complexity.
609 610
Protein- and fat-free matrices. Multiple regression of protein- and fat-free
611
matrices, i.e. matrices that only contain thickeners and / or disaccharides or ash
612
constituents, results in an R2 of 0.26 (SEE = 354, MAE = 197, i = 235; Fig. 4 (A)).
613
Neglecting limonene and diacetyl, as well as the upper and lower 2.5 % of the
614
remaining KMG-values, leads to an R² of 0.49 (SEE = 95.6, MAE = 63.8, i = 201; Fig.
615
4 (B)). In both cases, protein and milk fat content, as well as CWR and DWPD, were
616
not included as fitting parameters.
617 618
Protein-containing, fat-free matrices. If only matrices that contain protein, but
619
no fat, are respected, the multiple regression yields an R2 of 0.34 (SEE = 522, MAE =
620
257, i = 200; Fig. 5 (A)). Neglecting limonene and diacetyl, as well as the upper and ACS Paragon Plus Environment
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Journal of Agricultural and Food Chemistry
Page 26 of 66
621
lower 2.5 % of the remaining KMG-values, leads to an R2 of 0.50 (SEE = 119, MAE =
622
71.9, i = 139; Fig. 5 (B)).
623 624
Protein- and fat-containing matrices. Multiple regression of matrices that
625
contain both protein and fat results in an R2 of 0.44 (SEE = 1632, MAE = 1062, i =
626
105; Fig. 6 (A)). In diacetyl and limonene are excluded from the model, as well as the
627
upper and lower 2.5 % of the remaining KMG-values, an R2 of 0.78 (SEE = 845, MAE
628
= 574, i = 71; Fig. 6 (B)) is obtained.
629 630
Other than expected, the regression of the individual matrix types does not
631
lead to an improved R2, except for the protein- and fat-containing matrices. What is
632
more important, however, is the fact that both the SEE and the MAE are smaller for
633
the protein- and fat-free matrices, as well as for the protein-containing, fat-free
634
matrices. Given the lower magnitude of the respective KMG-values compared to
635
protein- and fat-containing matrices, this results in a more accurate forecast of the
636
partition coefficient.
637 638
Modeling selected aroma compounds. The neglect of diacetyl and
639
limonene, the by far most hydrophilic and hydrophobic compounds, considerably
640
improved the correlation coefficients of the above models, and decreased the error of
641
the estimates. As it will be seen below, this is not due to a lack of consistent data
642
concerning these two compounds, but a result of deviating behavior due to extreme
643
log P-values. This becomes evident when diacetyl and limonene are fitted separately.
644
For the regression of the single aroma compounds, log P, BP, and pi0 were not used
645
as fitting parameters.
646 ACS Paragon Plus Environment
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647 648
Journal of Agricultural and Food Chemistry
Diacetyl. Multiple regression of all available diacetyl KMG-values gives an R2 of 0.75 (SEE = 1017, MAE = 612, i = 56; Fig. 7).
649 650
Limonene. If the available limonene KMG-data is processed via multiple
651
regression, this results in an R2 of 0.98 (SEE = 55.6, MAE = 31.6, i = 51; Fig. 8). In
652
order to extent the validity of the model, both limonene varieties listed in Table 2
653
were used for the regression. It has to be mentioned that the high correlation
654
coefficient is of only limited significance, as 40 of the 51 KMG-values used for the
655
regression originate from the authors of this review. Consequently, the variability in
656
experimental conditions is very small, which reduces the respective spread.
657 658
Ethyl hexanoate. Ethyl hexanoate is the aroma compound for which the most
659
PRV determined K-values are available. It has been investigated over an
660
exceptionally wide range of parameter settings in ten different studies (Table 2), and
661
with a mean log P of +2.82, it does not show extreme physico-chemical properties.
662
Ethyl hexanoate therefore serves as a good indicator of PRV-data comparability.
663
Multiple regression of the available ethyl hexanoate KMG-values results in an R2 of
664
0.92 (SEE = 201.8, MAE = 98.3, i = 69; Fig. 9).
665
The improvement in R2, that was observed in undifferentiated modeling after
666
the neglect of diacetyl and limonene, was hence not due to a lack of consistent data
667
regarding these two aroma compounds. The problem most likely arouses from the
668
fact that these compounds, which are both located at the far end of the log P
669
spectrum (Fig. 1 (A)), show different matrix interaction mechanisms than the rest of
670
the investigated aroma compounds. In looking at Table 2, it becomes obvious that in
671
between a mean log P of +3.14 (linalool) and +4.48 to +4.51 (limonene), nearly no
672
dairy matrix relevant KMG-values are available. The same is true for the log P range ACS Paragon Plus Environment
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Journal of Agricultural and Food Chemistry
Page 28 of 66
673
of +0.72 (ethyl acetate) to -1.48 (diacetyl). These large gaps in one of the most
674
important physico-chemical parameters impede the creation of a uniform KMG
675
calculation model over the full range of log P-values.
676 677
Multiple regression of aroma compound partition coefficient values showed
678
that the available data, which originates from various, independently acting research
679
groups and was collected under highly different experimental conditions, is consistent
680
over a wide range of parameter settings. As the single-compound approach to KMG-
681
data modelling has shown, the PRV determined partition properties in aqueous,
682
polysaccharide and dairy matrices can be very well described. The PRV method
683
delivers reliable results that, although originating from substantially different studies,
684
can be processed via multiple regression using a limited number of independent
685
variables.
686
In which way, and to which extent, statistic procedures, such as forward or
687
backward elimination of parameters and potential outliers, can and should be used to
688
improve the accuracy of the fitted model and the forecast values, largely depends on
689
the goal and the subjects of the multiple regression. As it has been stated by
690
Piggott26, multiple regression is an art, not a science, and it is largely in the hands of
691
the user to exploit the data that was collected for this review.
692
However, future investigations should focus on a more systematic increase in
693
the complexity of the matrix. The majority of past PRV studies either dealt with
694
comparably simple, i.e. pure water, or highly complex matrices, i.e. polysaccharide
695
mixtures and dairy matrices composed of protein, fat, thickeners and added sucrose.
696
It was then tried to interpret the results based on earlier research, which was
697
predominantly concerned with single polysaccharide or protein constituents in water.
698
Although food research studies are often driven by the intention for industrial ACS Paragon Plus Environment
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Journal of Agricultural and Food Chemistry
699
application, researchers are encouraged to underlay their results on highly complex,
700
industry relevant matrices, with accompanying experiments that involve the single
701
constituents, probably by making use of adequate experimental designs. This would
702
greatly contribute to the understanding of the matrix constituent-aroma compound
703
interaction on a physico-chemical basis. In such supporting studies, more efforts
704
should be made to unmistakeably characterize the raw materials.
705
In the case of polysaccharide matrices, this would include, among other
706
polysaccharide-type specific parameters, the viscosity, the zeta-potential, the overall
707
ash content as well as the individual ion composition. In the case of protein-
708
containing matrices, next to dry matter and overall protein content, the pH in water,
709
the particle size in solution, the DWPD, the overall ash content and the process
710
history of the, mostly powdered, raw material should be specified. Only if the matrix
711
constituents are better specified, highly elaborated quantitative structure property
712
relationships (QSPR) as presented by Tromelin et al.65 for polysaccharide matrices
713
and Tromelin & Guichard66 for β-lactoglobulin, can unfold their potential in more
714
practical applications. In order to increase the reliability of KMG-value forecasts, a
715
sufficiently variable data pool is necessary, and so far does not exist for the majority
716
of the analysed aroma compounds. If the aroma retentive capacity of the matrix
717
constituents or of the matrix structure, and not the matrix’ sensory properties, are the
718
subject of aroma compound partition research, it is therefore recommended to
719
incorporate as many aroma compounds into the matrix as analytically feasible. These
720
compounds should be selected with especial regard to their log P-values and their
721
aroma contribution properties. More data is needed especially in the log P range of -
722
1.5 to +1.0 and +3.0 to +4.5. A recent study reveals partition coefficients for aroma
723
compounds within a log P range of +3.0 to +4.5 in water and in acacia gum
724
solutions.67 Moreover, the authors have distinguished the affinity of aroma ACS Paragon Plus Environment
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Page 30 of 66
725
compounds for water (solubility) from the affinity for the lipid phase (log P). This
726
original approach made possible to understand the retention phenomena in
727
polysaccharide solution and could be applied to dairy matrices as well.
728
Furthermore, as it is seen from both Fig. 1 (B) and the observed versus
729
predicted plots displayed above, more KMG-values in the < 50 and the > 500 range
730
are needed. Future studies should both improve and adjust the experimental
731
conditions that allow for the determination of partition coefficients of this magnitude,
732
especially regarding the optimization of the phase ratio β and the application of
733
different equilibration temperatures.
734
It is also recommended that results of KMG-value determination are not only be
735
presented as retention ratios relative to a reference matrix, as it is the case in some
736
studies15,37,40,52, but also tabulated, which would further extend the available data
737
base. We furthermore agree with Kühn et al.28, that in order to elevate the practical
738
value of the results, future PRV studies should aim to connect their results to
739
orthonasal sensory analysis. This could be achieved by descriptive sensory analysis
740
or at least triangle testing. Triangle testing can be easily performed on selected
741
matrices that are aromatised with only a single compound, using combinations that
742
showed considerable changes in the KMG-value. However, it must be checked that
743
non-aromatised matrices of varying composition are not recognised as different due
744
to their inherent aroma profile.
745 746
Abbreviations used
747
PRV, phase ratio variation; RP-HPLC, reverse phase high performance liquid
748
chromatography; DWPD, degree of whey protein denaturation; CWR, casein protein
749
to whey protein ratio; ACC, aroma compound concentration; CAS, chemical abstract
750
service;
BP,
boiling
point;
M,
molecular
mass;
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751
chromatography; FID, flame ionization detector; SEE, standard error of estimation;
752
MAE, mean absolute error; PSD, particle size distribution; AMF, anhydrous milk fat;
753
MCT, medium chain triglyceride
754 755
Acknowledgments
756
The authors thank Bernd Köhlnhofer from Zott SE & Co. KG and Katja Buhr from
757
SGS Institute Fresenius Austria GmbH for the close cooperation within this research
758
project on the aroma-dairy matrix interaction.
759 760 761 762
Author information
763
Corresponding author
764
*Dr. Alina Sonne. Tel: +49 711 459 23616. Fax: +49 711 459 23617.
765
E-mail:
[email protected] 766
Funding
767
This
768
Ernährungsindustrie e.V., Bonn), the AiF and the Ministry of Economics and
769
Technology. AiF-Project No.: 15158 N.
770
Notes
771
The authors declare no competing financial interest.
research
project
was
supported
by
the
FEI
(Forschungskreis
der
772 773
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Figure captions Fig. 1: Magnitude of the (A) 56 aroma compound log P values and (B) 542 matrix/gas partition coefficients KMG that were extracted from the reviewed literature (Table 2). Dashed lines border the upper and lower 2.5 %, 5.0 % and 12.5 % percent of log P and KMG-values, respectively. Fig. 2: KMG observed versus predicted plot obtained by multiple regression of (A) all KMG-values listed in Table 2 (excluding lines 52 and 439) and (B) 95 % of these values, excluding the upper and lower 2.5 %. For Fig. 2 (A) and (B), the following relationship between the fitting parameters and KMG is derived within the boundaries given in Table 5: 2 (A): KMG = 3491 – (393.7*log P) – (0.799*BP) – (19.52*pi0) + (6.861*Water) + (49.57*Protein) + (75.81*Fat) + (2.674*CWR) – (138.4*Thickeners) – (59.55*Disaccharides) – (48.33*Ash) – (1.303*DWPD) + (151.7*pH) – (87.15*ϑ); 2 (B): KMG = 2459 – (168.2*log P) – (3.075*BP) – (11.68*pi0) + (0.100*Water) + (19.18*Protein) + (49.59*Fat) + (2.043*CWR) – (90.59*Thickeners) + (6.191*Disaccharides) + (233.7*Ash) – (0.709*DWPD) + (62.81*pH) – (40.13*ϑ).
Fig. 3: KMG observed versus predicted plot obtained by multiple regression of all KMG-values listed in Table 2 (excluding lines 52 and 439), with the exception of diacetyl and limonene KMG-values, as well as the remaining upper and lower 2.5 %. Within the in Table 5 listed parameter boundaries, KMG can be calculated as follow: KMG = 3199 – (338.2*log P) – (6.404*BP) – (8.357*pi0) + (24.59*Water) + (4.839*Protein) + (44.01*Fat) + (0.545*CWR) – (66.48*Thickeners) – (3.362*Disaccharides) + (141.4*Ash) – (0.490*DWPD) + (24.94*pH) – (26.69*ϑ).
Fig. 4: KMG observed versus predicted plot obtained by multiple regression of (A) all KMG-values determined in protein- and fat-free matrices (excluding lines 52 and 439) and (B) with the exception of diacetyl and limonene KMG-values, as well as the remaining upper and lower 2.5 %. Within the in Table 5 listed parameter boundaries, KMG can be calculated as follow: KMG = 28019 – (151.4*log P) + (3.415*BP) – (0.037*pi0) - (278.0*Water) - (233.4*Thickeners) – (279.0*Disaccharides) – (273.3*Ash) – (30.24*pH) – (2.346*ϑ).
Fig. 5: KMG observed versus predicted plot obtained by multiple regression of (A) all KMG-values determined in protein-containing, fat-free matrices and (B) with the exception of diacetyl and limonene KMG-values, as well as the remaining upper and lower 2.5 %. Within the in Table 5 listed parameter boundaries, KMG in protein-containing, fat-free matrices (Fig. 5B) can be calculated as follow: KMG = 688.2 – (241.8*log P) + (4.275*BP) – (2.811*pi0) – (1.953*Water) + (1.108*Protein) + (0.033*CWR) – (3.987*Thickeners) – (5.948*Disaccharides) – (19.47*Ash) – (0.121*DWPD) – (17.88*pH) – (8.516*ϑ).
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Fig. 6: KMG observed versus predicted plot obtained by multiple regression of (A) all KMG-values determined in protein- and fat-containing matrices and (B) with the exception of diacetyl and limonene KMG-values, as well as the remaining upper and lower 2.5 %. Within the in Table 5 listed parameter boundaries, KMG in protein- and fat-containing matrices (Fig. 6 (B)) can be calculated as: KMG = 186867 – (1972*log P) + (73.55*BP) + (7.355*pi0) – (1677*Water) – (5654*Protein) – (1621*Fat) + (166.0*CWR) – (4432*Thickeners) – (540.2*Disaccharides) – (14153*Ash) – (0.641*DWPD) – (178.2*pH) – (133.4*ϑ).
Fig. 7: KMG observed versus predicted plot obtained by multiple regression of all diacetyl KMG-values. Within the in Table 5 listed parameter boundaries, KMG of diacetyl is calculated as: KMG = – 472801 + (4768*Water) + (4692*Protein) + (4607*Fat) – (7.672*CWR) – (6192*Thickeners) + (4085*Disaccharides) + (9597*Ash) + (0.410*DWPD) + (550.3*pH) – (162.4*ϑ).
Fig. 8: KMG observed versus predicted plot obtained by multiple regression of all limonene KMG-values. Within the in Table 5 listed parameter boundaries, KMG of limonene can be calculated as: KMG = 38281 – (411.6*Water) – (369.8*Protein) – (311.8*Fat) + (0.098*CWR) + (38.91*Thickeners) + (78.64*Disaccharides) – (1745*Ash) + (0.253*DWPD) – (14.39*pH) + (35.03*).
Fig. 9: KMG observed versus predicted plot obtained by multiple regression of all ethyl hexanoate KMGvalues. Within the in Table 5 listed parameter boundaries, KMG of ethyl hexanoate can be calculated as: KMG = 8156 – (80.89*Water) – (70.08*Protein) + (30.11*Fat) + (0.070*CWR) – (175.1*Thickeners) + (29.13*Disaccharides) – (215.1*Ash) – (0.569*DWPD) – (7.536*pH) – (12.80*ϑ).
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Table 1: Concentration and supplier specification of the strawberry aroma-based composition of volatile compounds used in this study. Compound
% (w/w) in aroma
b
ppm (w/w)
log P
in final product
octanol/water
CAS-No. a
Limonene
1.20
120
+ 4,5
138-86-3
Ethyl hexanoate
0.60
60
+ 2,8
123-66-0
(Z)-3-hexenyl acetate
0.60
60
+ 2,4
3681-71-8
γ-decalactone
0.30
30
+ 2,4
706-14-9
Methyl cinnamate
0.30
30
+ 2,2
1754-62-7
Ethyl-2-methylbutanoate
1.00
100
+ 2,1
7452-79-1
Hexanoic acid
1.80
180
+ 1,8
142-62-1
Ethyl butanoate
0.80
80
+ 1,8
105-54-4
(Z)-3-hexenol
1.00
100
+ 1,6
928-96-1
2-methyl butyric acid
1.80
180
+ 1,1
116-53-0
Furaneol
0.27
27
+ 0,3
3658-77-3
b
1.20
120
- 1,3
431-03-8
89.13
-
Diacetyl
Propylene glycol
948 949 950 951 952
Page 40 of 66
-
57-55-6
The CAS-No. is a unique numerical identifier assigned by Chemical Abstracts Service to every chemical substance. a
log P (hydrophobicity) calculated by (ACD/Labs) Software V8.14 (Advanced Chemistry Development
Inc., Toronto, Canada). b
Compounds were added to commercial strawberry aroma.
ACS Paragon Plus Environment
40
Page 41 of 66
Journal of Agricultural and Food Chemistry
Table 2: Literature review of matrix/gas partition coefficients KMG of various aroma compounds, determined by the phase ratio variation (PRV) method, in aqueous, polysaccharide and dairy matrices. Aroma compound a denomination & specification Trivial name CAS-No. D.L-Limonene 138-86-3
Molecular structure
Aroma compound physico-chemical propertiesb log P range M (-) (mean value) BP (°C) pi0 (hPa) 4.45-4.57 136 (4.51) 178 2.05
(Dairy) matrix Compositionc ACC (ppm w/w) 120 120
Water (% w/w)
f Protein (% w/w)
g Milk fat (% w/w)
95.2 95.2
0.0 0.0
120 120 120 120 120 120 120 200 120 200 200 200 200 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120
91.2 91.0 90.7 90.4 90.1 90.1 90.1 90.1 90.1 90.1 90.1 90.1 90.1 89.9 88.5 87.6 85.1 85.1 85.1 85.1 82.6 82.6 81.8 81.8 80.1 80.1 80.1 80.1
4.0 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 6.0 6 6.0 6 8.0 6 8.0 6 8.0 6 8.0 6 12.0 6 12.0 6 12.0 6 12.0 6 12.0 6 12.0 6 12.0 6 12.0
200 200 120 200 120 120 200 200 200 120
89.7 89.2 88.2 88.1 86.3 86.3 86.3 86.3 86.3 78.7
6 6
6
4.0 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6
h
(Dairy) matrix Processingd
CWR (-)
i Thick. (% w/w)
j Disacc. (% w/w)
0.0 0.0
n.a. n.a.
0.00 0.00
1
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.3 0.7 1.5 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 100.0 0.7 4.0 4.0 4.0 4.0 4.0 0.3 0.3 0.7 0.7 4.0 4.0 4.0 4.0
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2 0.05 2 0.10 2 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
1
0.5 1.0 2.0 2.0 4.0 4.0 4.0 4.0 4.0 12.0
4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0
0.00 0.00 0.00 2 0.10 0.00 0.00 0.00 0.00 0.00 0.00
4.4 4.4
k Ash (% w/w)
l
4.07 9.68 13.75 18.04 22.70 30.48 43.43 27.71 25.12 18.56 30.28 31.22 25.78 24.21 49.83 29.27 44.25 39.67 40.33 80.40 28.47 40.47 38.15 70.20 61.69 76.33 75.25 80.64
40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40
3 3 3 3 3 92 57 92 99 3
6.75 6.75 6.75 6.75 6.75 1 4.23 6.75 6.75 6.75 6.75
83.22 148.80 273.04 301.33 392.97 610.67 508.37 374.26 375.89 918.22
40 40 40 40 40 40 40 40 40 40
1
0.79 0.78 0.78 0.78 0.77 0.77 0.77 0.77 0.77 0.74
5.0 5.0 5.0 1 5.0 1 4.9 1 4.9 1 4.9 1 4.9 1 4.9 1 4.5
Temp. First author (°C)
6.75 6.75 6.75 6.75 6.75 1 4.23 1 4.23 6.75 6.75 6.75 6.75 6.75 6.75 6.75 6.75 6.75 6.75 1 4.23 6.75 1 4.23 6.75 1 4.23 6.75 1 4.23 6.75 1 4.23 6.75 1 4.23
0.49 0.57 0.64 0.72 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.86 0.75 0.97 1.15 1.15 1.15 1.15 0.85 0.85 1.07 1.07 1.51 1.51 1.51 1.51
1
KMG (-)
3 3 3 3 3 3 92 57 92 99 3 3 57 3 92 3 3 3 92 92 3 3 3 3 3 3 92 92
4.3 4.5 1 4.7 1 4.9 1 5.1 1 5.1 1 5.1 1 5.1 1 5.1 1 5.1 1 5.1 1 5.1 1 5.1 1 5.3 1 4.8 1 5.4 1 5.7 1 5.7 1 5.7 1 5.7 1 4.6 1 4.6 1 5.2 1 5.2 1 6.4 1 6.4 1 6.4 1 6.4 1
1
n
2.67 2.69
n.a. n.a.
1
pH (-)
4.23 6.75
0.43 0.43
1
m
DWPD (%)
Reported temperature-specific matrix/gas partition coefficiente
40 Heilig 40 Heilig
Line
Year
* *
1 2
Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig
201115 201115 201115 201115 201115 201115 * * * * * * * 15 2011 * * 15 2011 15 2011 * * * * * * 201115 15 2011 * *
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig
* * * * * * * * * *
31 32 33 34 35 36 37 38 39 40
41 ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
D-Limonene 5989-27-5
Decanoic acid 334-48-5
Ethyl octanoate 106-32-1
4.45-4.50 (4.48)
4.09 (4.09)
3.83-3.90 (3.87)
136 178 2.05
172 269 0.00
15 50 15 15 15 15 15 15 15 15
100.0 100.0 100.0 63.0 63.4 99.2 64.8 99.7 63.1 62.6
2
77.5
50 1
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3
100.0 77.5
5.4 0.0
3
5.4
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1
4.0
0.0 1
4.0
n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
0.00 0.00 3 0.05 0.00 0.00 3 0.80 0.00 3 0.33 0.00 0.00
2.1
0.00
n.a. 2.1
172 207 0.30
8 50 8 8 8
100.0 100.0 63.1 63.1 63.1
0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a. n.a.
2-Decanone 693-54-9
3.73 (3.73)
156 210 0.36
33
100.0
0.0
0.0
1.0
Nonanal 204-688-5
3.36-3.46 (3.41)
142 192 0.71
20 20 20 20
100.0 89.6 89.6 89.6
0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a.
1-Nonanol 143-08-8
3.30-3.39 (3.35)
144 214 0.05
12 12 12
100.0 99.6 98.8
0.0 0.0 0.0
0.0 0.0 0.0
n.a. n.a. n.a.
12 12 12
86.4 86.3 86.2
4.0 4.0 2 4.0
0.0 0.0 0.0
4.0 4.0 4.0
13 50 13 13 1000 13 13 13 13 13 13 13
100.0 100.0 100.0 99.6 99.0 98.8 98.6 64.8 63.4 63.1 63.0 62.6
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
13 13 13
86.4 86.3 86.2
4.0 4.0 2 4.0
0.0 0.0 0.0
4.0 4.0 4.0
Linalool 78-70-6
2.91-3.40 (3.14)
154 195 0.12
2 2
2 2
0.0 0.0 0.0 2 35.0 2 35.0 0.0 2 35.0 0.0 2 35.0 2 35.0
0.00 0.00 0.00 0.19 0.19 0.00 0.19 0.00 0.19 0.19
n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
3
1.20
n.a.
11.8
0.00 0.00 0.00 0.00 1.73 5 1.73 5 1.73 5
0.0 3
11.8
0.00 0.40 2 0.40 2 0.40
0.00 0.05 2 0.10
7.00
1
2
4.60
2061.86 33112.6
1
8771.93
4 Saint-Eve** 25 Atlan 4 Saint-Eve**
200647 14 2006 200647 200647 47 2006 200647 200647 200647 200647 200647
41 42 43 44 45 46 47 48 49 50
200645
51
2006
14
52
2006
45
53
47
2006 200614 200647 200647 47 2006
54 55 56 57 58
7.00 7.00 7.00 7.00 7.00
29.07 49.75 28.99 1 30.77 1 32.43
30 25 30 20 10
0.0
0.00
n.a.
7.00
1
40 Benjamin
201137
59
0.0 10.0 2 10.0 2 10.0
0.00 0.00 0.02 0.04
n.a. n.a. n.a. n.a.
7.00 7.00 7.00 7.00
1
37 37 37 37
Bylaite Bylaite Bylaite Bylaite
200325 200325 25 2003 200325
60 61 62 63
0.00 0.62 3 0.62
n.a. n.a. n.a.
6.50 3.50 3.50
83.33 82.64 84.03
30 Merabtine 30 Merabtine 30 Merabtine
201029 201029 29 2010
64 65 66
5.7 5.8 3 5.8
0.86 0.86 0.86
85 85 85
4.00 4.00 1 4.00
400.00 250.00 166.67
30 Merabtine 30 Merabtine 30 Merabtine
201029 201029 29 2010
67 68 69
0.0 0.0 0.0 0.0 0.0 2 0.2 0.0 2 35.0 2 35.0 2 35.0 2 35.0 2 35.0
0.00 0.00 0.00 3 0.62 0.00 3 0.62 0.00 0.19 0.19 0.19 0.19 0.19
n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
6.50 7.00 7.00 3.50 7.00 3.50 7.00 7.00 7.00 7.00 7.00 7.00
333.33 313.48 1 258.40 285.71 1 1265.82 256.41 1 280.90 1 254.45 1 310.08 1 313.73 1 310.08 1 296.30
30 25 30 30 7 30 30 30 30 30 30 30
Merabtine Atlan Savary Merabtine Deleris** Merabtine Savary Savary Savary Savary Savary Savary
201029 200614 200647 201029 200746 29 2010 47 2006 47 2006 47 2006 47 2006 47 2006 47 2006
70 71 72 73 74 75 76 77 78 79 80 81
0.86 0.86 0.86
85 85 85
30 Merabtine 30 Merabtine 30 Merabtine
2010 29 2010 29 2010
29
82 83 84
2
2
2
1
Savary Atlan Savary Savary Savary Savary Savary Savary Savary Savary
n.a. n.a. n.a. n.a. n.a.
0.00 0.05 2 0.10 0.00 0.00 0.00 2 0.10 1 1.00 2 0.80 4 1.40 0.00 5 1.45 5 1.73 6 1.80 5 2.20
80
4.60
2
30 25 30 30 30 30 30 30 30 30
0.00 0.00 0.19 0.19 0.19
0.0 0.0 2 0.2
2
1.20
n.a.
1
0.0 0.0 35.0 2 35.0 2 35.0 2
0.00 0.10 2 0.80 2
0.00
1 3.98 96.15 1 390.24 1 400.00 1 410.26 1 411.52 1 411.52 1 416.67 1 450.70 1 457.14
7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00
1
0.00
2
Page 42 of 66
3
1
5.7 5.8 3 5.8 3
3
1 1
1
4.00 4.00 1 4.00 1
1 1
19.66
10.53 1 7.42 1 13.79 1 10.06
1
285.71 263.16 250.00
Savary Atlan Savary Savary Savary
42 ACS Paragon Plus Environment
Page 43 of 66
Journal of Agricultural and Food Chemistry
1000 1000 1000 1000 1000 1000 1000 1000 1000
81.8 89.6 79.6 77.5 77.5 77.5 77.5 77.5 77.5
2
5.4 4.4 2 5.4 2 5.4 2 5.4 4 5.4 4 5.4 3 5.4 2 5.4
0.0 0.1 1 2.0 1 4.0 1 4.0 1 4.0 1 4.0 1 4.0 1 4.0
4.0 4.0 4.0 4.0 4.0 5.2 5.2 2.0 4.0
1
E-2-nonenal 18829-56-6
2.96-3.17 (3.07)
140 189 0.34
40 40 40 40
100.0 89.6 89.6 89.6
0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a.
Nonan-2-one 821-55-6
2.90-3.03
142
3
100.0
0.0
0.0
n.a.
(2.97)
195 0.86
3 3 3 3
85.1 84.6 84.3 83.6
6
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.40 2 0.40 2 0.40 2
0.0 0.0 0.0 0.0
3.0 3.0 3.0 3.0
0.00 0.00 0.00 0.00
6
2
1
2
1
7.00
1
1
17.7 17.7 17.7 1 17.7
2.14 3.14 3.14 2 3.14
65 65 65 65
1
2
1
2
100.00 150.15 200.00 1 150.15
30 30 30 30
1
176.99
40 Benjamin
201137
103
27.78 14.81 22.22 22.73 1 17.64 1 19.16 1 20.70
30 37 30 30 37 37 37
Merabtine Bylaite Merabtine Merabtine Bylaite Bylaite Bylaite
201029 200325 29 2010 201029 200325 200325 200325
104 105 106 107 108 109 110
30 Merabtine 30 Merabtine 30 Merabtine
201029 201029 201029
111 112 113
30 25 30 7 25 10 40 40 25 10 20 30
Gierczynski Atlan Savary Deleris** Deleris** Lauverjat Heilig Heilig Deleris** Savary Savary Savary
2007 200614 200647 46 2007 48 2008 5 2009 * * 48 2008 47 2006 200647 200647
30
114 115 116 117 118 119 120 121 122 123 124 125
30 40 40 40 40
Martuscelli Heilig Heilig Heilig Heilig
200824 201115 201115 201115 201115
126 127 128 129 130
1
4
1
Octanal 124-13-0
2.80-2.95 (2.87)
128 172 2.75
11 20 11 11 20 20 20
100.0 100.0 99.6 98.8 89.6 89.6 89.6
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a. n.a. n.a. n.a.
0.00 0.00 2 0.10 2 0.80 2 0.40 2 0.40 2 0.40
0.0 0.0 0.0 2 0.2 2 10.0 2 10.0 2 10.0
0.00 0.00 3 0.62 3 0.62 0.00 1 0.02 1 0.04
n.a. n.a. n.a. n.a. n.a. n.a. n.a.
6.50 7.00 3.50 3.50 7.00 7.00 7.00
11 11 11
86.4 86.3 86.2
4.0 4.0 4.0
0.0 0.0 0.0
4.0 4.0 4.0
5.7 5.8 5.8
0.86 0.86 0.86
85 85 85
1 50 155 1000
155 155 155
100.0 100.0 100.0 99.0 99.0 99.0 95.2 95.2 64.0 63.1 63.1 63.1
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
0.0 0.0 0.0 0.0 0.0 0.0 1 4.4 1 4.4 2 35.0 2 35.0 2 35.0 2 35.0
0.00 0.00 0.00 0.00 0.00 0.00 0.43 0.43 0.00 0.19 0.19 0.19
n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
7.00 7.00 7.00 7.00 7.00 7.00 1 4.23 6.75 7.00 7.00 7.00 7.00
120 60 60 60 60
82.8 91.2 91.0 90.7 90.4
3.2 4.0 6 4.0 6 4.0 6 4.0
0.0 0.0 0.0 0.0 0.0
4.0 0.0 0.3 0.7 1.5
0.68 0.49 0.57 0.64 0.72
99 3 3 3 3
7.00 6.75 6.75 6.75 6.75
1 3
2
3
4.01 0.00 0.00 0.00 0.00
3
9.4 4.3 1 4.5 1 4.7 1 4.9 1
50.00
6.75 4.60 4.60 4 4.60 4
7.00
2
1
8 7 8 7 7 7 7 7 8
1
n.a.
5
99 100 101 102
n.a.
0.00
6
2007 30 2007 200730 200730
0.00
0.0
1
98
30
0.0
0.00
9 60 60
200730
37 37 37 37
n.a.
0.00 0.00 0.00 1 1.00 1 1.00 1 1.00 0.00 0.00 1 1.00 5 1.73 5 1.73 5 1.73
30 Gierczynski
49.75 36.90 1 50.25 1 40.32
0.0
144 167 2.21
94 95 96 97
1
0.0
2.80-2.84 (2.82)
25
7.00 7.00 7.00 7.00
100.0
Ethyl hexanoate 123-66-0
2003 200325 200325 200325
n.a. n.a. n.a. n.a.
33
0.00 0.05 0.10
Bylaite Bylaite Bylaite Bylaite
0.00 0.00 1 0.02 1 0.04
130 195 0.20
2
85 86 87 88 89 90 91 92 93
0.0 10.0 2 10.0 2 10.0
2.88-3.00 (2.94)
2
200936 200746 200936 200746 200746 200746 200746 200746 200936
80 90 80 80 80 80 80 80 80
Octanol 111-87-5
2
Deleris** Deleris** Deleris** Deleris** Deleris** Deleris** Deleris** Deleris** Deleris**
1.16 0.94 1.18 1.20 1.20 1.20 1.20 1.20 1.20
2
4.60 4.00 2 4.60 2 4.60 2 4.60 2 4.60 2 4.60 2 4.60 2 4.60
3571.43 2564.10 1 4166.67 1 1724.14 1 1123.60 1 4761.90 1 3846.15 1 2439.02 1 9090.91
11.6 1 5.0 3 11.8 3 12.5 3 12.5 3 12.5 3 12.5 3 12.5 3 11.9
0.00
10.0 10.0 10.0 6 10.0 6
3
1
1
66.67 71.43 68.97
4.00 4.00 4.00
1 1
1
20.83 30.40 1 33.11 1 117.10 1 21.65 1 133.69 9.50 12.59 1 61.35 1 63.16 1 38.71 1 33.33 1
1
57.47 31.31 23.70 35.00 19.62
Gierczynski Gierczynski Gierczynski Gierczynski
43 ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
60 60 60 100 60 100 100 100 100 60 1000 60 60 60 60 60 60 1 1 1 1 60 60 60 60 60 60 60 60
90.1 90.1 90.1 90.1 90.1 90.1 90.1 90.1 90.1 89.9 81.8 88.5 87.6 85.1 85.1 85.1 85.1 85.1 84.6 84.3 83.6 82.6 82.6 81.8 81.8 80.1 80.1 80.1 80.1
1000 100 100 60 100 1000 120 60 60 100 100 100 22 22 22 1000 1000 1000 1000 1000 1000 1000 60
89.6 89.7 89.2 88.2 88.1 79.6 80.2 86.3 86.3 86.3 86.3 86.3 77.5 77.5 77.5 77.5 77.5 77.5 77.5 77.5 77.5 77.5 78.7
6
4.0 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 2 5.4 6 6.0 6 6.0 6 8.0 6 8.0 6 8.0 6 8.0 6 10.0 6 10.0 6 10.0 6 10.0 6 10.0 6 12.0 6 12.0 6 12.0 6 12.0 6 12.0 6 12.0 6 12.0 6
1
4.4 4.0 6 4.0 6 4.0 6 4.0 2 5.4 1 3.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 4 5.4 2 5.4 3 5.4 2 5.4 2 5.4 4 5.4 4 5.4 3 5.4 3 5.4 2 5.4 6 4.0 6
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 100.0 4.0 0.7 4.0 4.0 4.0 4.0 4.0 3.0 3.0 3.0 3.0 0.3 0.3 0.7 0.7 4.0 4.0 4.0 4.0
0.00 0.00 0.00 0.00 0.00 0.00 2 0.05 2 0.10 2 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.1 0.5 1.0 2.0 2.0 2.0 1 2.7 4.0 4.0 4.0 4.0 4.0 4.0 1 4.0 1 4.0 1 4.0 1 4.0 1 4.0 1 4.0 1 4.0 1 4.0 1 4.0 12.0
4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.5 4.0 2.1 4.0 4.0 5.2 5.2 2.0 2.0 4.0 4.0
0.00 0.00 0.00 0.00 2 0.10 0.00 5 4.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
1
5.1 5.1 1 5.1 1 5.1 1 5.1 1 5.1 1 5.1 1 5.1 1 5.1 1 5.3 3 11.6 1 4.8 1 5.4 1 5.7 1 5.7 1 5.7 1 5.7 1 17.7 1 17.7 1 17.7 1 17.7 1 4.6 1 4.6 1 5.2 1 5.2 1 6.4 1 6.4 1 6.4 1 6.4 1
1
5.0 5.0 1 5.0 1 5.0 1 5.0 3 11.8 3 9.3 1 4.9 1 4.9 1 4.9 1 4.9 1 4.9 3 11.8 3 11.8 3 11.8 3 12.5 3 12.5 3 12.5 3 12.5 3 12.5 3 12.5 3 11.9 1 4.5 1
Page 44 of 66
0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.86 1.16 0.75 0.97 1.15 1.15 1.15 1.15 2.14 2 3.14 2 3.14 2 3.14 0.85 0.85 1.07 1.07 1.51 1.51 1.51 1.51
3 3 92 57 92 99 3 3 57 3 80 92 3 3 3 92 92 65 65 65 65 3 3 3 3 3 3 92 92
0.94 0.79 0.78 0.78 0.78 1.18 0.67 0.77 0.77 0.77 0.77 0.77 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 0.74
90 3 3 3 3 80 99 3 92 57 92 99 80 80 80 80 80 80 80 80 80 80 3
6.75 4.23 1 4.23 6.75 6.75 6.75 6.75 6.75 6.75 6.75 2 4.60 6.75 6.75 6.75 1 4.23 6.75 1 4.23 6.75 4 4.60 4 4.60 4 4.60 6.75 1 4.23 6.75 1 4.23 6.75 1 4.23 6.75 1 4.23
27.01 36.03 33.85 31.63 28.52 15.50 28.59 25.24 24.74 25.18 1 261.78 56.25 39.38 59.75 64.20 44.67 34.47 1 36.36 1 66.67 1 71.43 1 71.43 86.92 94.37 79.45 63.73 73.04 77.33 76.99 85.02
40 40 40 40 40 40 40 40 40 40 8 40 40 40 40 40 40 30 30 30 30 40 40 40 40 40 40 40 40
Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Deleris** Heilig Heilig Heilig Heilig Heilig Heilig Gierczynski Gierczynski Gierczynski Gierczynski Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig
201115 201115 * * * * * * * 15 2011 36 2009 * * 15 2011 15 2011 * * 30 2007 30 2007 30 2007 200730 * * * * 201115 201115 * *
131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159
2
1
7 40 40 40 40 8 30 40 40 40 40 40 4 4 4 7 7 7 7 7 7 8 40
Deleris** Heilig Heilig Heilig Heilig Deleris** Martuscelli Heilig Heilig Heilig Heilig Heilig Saint-Eve** Saint-Eve** Saint-Eve** Deleris** Deleris** Deleris** Deleris** Deleris** Deleris** Deleris** Heilig
200736 * * * * 200936 200824 * * * * * 45 2006 45 2006 200645 200746 200746 200746 200746 46 2007 46 2007 36 2009 *
160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182
1
4.00 6.75 6.75 6.75 6.75 2 4.60 7.00 6.75 1 4.23 6.75 6.75 6.75 2 4.60 2 4.60 2 4.60 2 4.60 2 4.60 2 4.60 2 4.60 2 4.60 2 4.60 2 4.60 6.75
258.40 43.18 96.78 182.67 189.67 1 869.57 1 578.03 343.91 410.64 322.16 229.85 260.61 1 2331.00 1 1845.02 1 1499.25 1 1369.86 1 1562.50 1 1562.50 1 1818.18 1 1612.90 1 1538.46 1 1785.71 1056.88
44 ACS Paragon Plus Environment
Page 45 of 66
3-Octanol 589-98-0
Isopropyl tiglate 1733-25-1
Journal of Agricultural and Food Chemistry
2.70-2.82 (2.76)
2.60-2.75 (2.68)
130 174 0.68
142 n.a. 2.50
11 11 11
100.0 99.6 98.8
11 11 11
86.4 86.3 86.2
13 13 13
100.0 99.6 98.8
13 13 13
86.4 86.3 86.2
0.0 0.0 0.0
0.0 0.0 0.0
n.a. n.a. n.a.
4.0 4.0 2 4.0
0.0 0.0 0.0
4.0 4.0 4.0
0.0 0.0 0.0
0.0 0.0 0.0
n.a. n.a. n.a.
4.0 4.0 2 4.0
0.0 0.0 0.0
4.0 4.0 4.0
2 2
2 2
E,E-2,4-nonadienal 5910-87-2
2.55-2.65 (2.60)
138 n.a. 0.14
40 40 40 40
100.0 89.6 89.6 89.6
0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a.
E,E-2,6-nonadienal 17587-33-6
2.55-2.60 (2.58)
138 203 0.37
40 40 40 40
100.0 89.6 89.6 89.6
0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a.
γ-deca-lactone 706-14-9
2.39-2.72 (2.56)
170 267 0.01
50
100.0
0.0
0.0
n.a.
E-2-octenal 2548-87-0
2.44-2.64 (2.54)
126 n.a. 0.73
40 40 40 40
100.0 89.6 89.6 89.6
0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a.
(Z)-3-hexenyl acetate 3681-71-8
2.40-2.63 (2.48)
142 166 1.62
60 60
95.2 95.2
0.0 0.0
0.0 0.0
n.a. n.a.
30 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60
82.8 90.1 90.1 90.1 90.1 88.5 87.6 85.1 85.1 85.1 85.1 82.6 82.6 81.8 81.8 80.1
3.2 4.0 6 4.0 6 4.0 6 4.0 6 6.0 6 6.0 6 8.0 6 8.0 6 8.0 6 8.0 6 12.0 6 12.0 6 12.0 6 12.0 6 12.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4.0 4.0 4.0 4.0 4.0 0.7 4.0 4.0 4.0 4.0 4.0 0.3 0.3 0.7 0.7 4.0
1 6
0.00 0.10 2 0.80
0.0 0.0 2 0.2
186 187 188
6.50 3.50 3.50
30.77 26.67 26.74
30 Merabtine 30 Merabtine 30 Merabtine
201029 29 2010 201029
189 190 191
37.04 36.36 35.71
30 Merabtine 30 Merabtine 30 Merabtine
2010 201029 201029
29
192 193 194
192.31 196.08 1 416.67 1 217.39
37 37 37 37
Bylaite Bylaite Bylaite Bylaite
200325 25 2003 200325 200325
195 196 197 198
7.00 7.00 7.00 7.00
1
188.68 222.22 1 256.41 1 250.00
37 37 37 37
Bylaite Bylaite Bylaite Bylaite
200325 200325 200325 25 2003
199 200 201 202
n.a.
7.00
1
25 Atlan
200614
203
0.00 0.00 1 0.02 1 0.04
n.a. n.a. n.a. n.a.
7.00 7.00 7.00 7.00
37 37 37 37
200325 25 2003 200325 200325
204 205 206 207
0.43 0.43
n.a. n.a.
* *
208 209
0.68 0.79 0.79 0.79 0.79 0.75 0.97 1.15 1.15 1.15 1.15 0.85 0.85 1.07 1.07 1.51
99 92 92 3 3 92 3 92 92 3 3 3 3 3 3 92
24
210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225
0.0 10.0 2 10.0 2 10.0
0.00 0.00 1 0.02 1 0.04
n.a. n.a. n.a. n.a.
7.00 7.00 7.00 7.00
1
0.0 10.0 2 10.0 2 10.0
0.00 0.00 1 0.02 1 0.04
n.a. n.a. n.a. n.a.
0.0
0.00
0.0 10.0 2 10.0 2 10.0
3
2
2
0.00
4.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
29
85 85 85
1
5
2010 201029 201029
0.86 0.86 0.86
0.00 0.05 2 0.10 2
0.00 0.00
30 Merabtine 30 Merabtine 30 Merabtine
5.7 5.8 3 5.8
0.0 0.0 2 0.2
0.00 0.40 2 0.40 2 0.40
250.00 222.22 200.00
n.a. n.a. n.a.
0.00 0.10 2 0.80
5.7 5.8 3 5.8
2
2
4.00 4.00 1 4.00
0.00 0.62 3 0.62
3
0.00 0.40 2 0.40 2 0.40
183 184 185
85 85 85
1
2
201029 29 2010 201029
0.86 0.86 0.86
0.00 0.05 2 0.10
0.00 0.40 2 0.40 2 0.40
30 Merabtine 30 Merabtine 30 Merabtine
n.a. n.a. n.a.
2
2
222.22 181.82 178.57
0.00 0.62 3 0.62
2
2
1
4.4 4.4
1
3
9.4 5.1 1 5.1 1 5.1 1 5.1 1 4.8 1 5.4 1 5.7 1 5.7 1 5.7 1 5.7 1 4.6 1 4.6 1 5.2 1 5.2 1 6.4 1
3
3
6.50 3.50 3.50 1 1
1
4.00 4.00 1 4.00 1
1
1
296.74
1
73.53 62.50 1 59.17 1 70.92 1
1
40.00 41.88
4.23 6.75
7.00 6.75 1 4.23 6.75 1 4.23 6.75 6.75 6.75 1 4.23 6.75 1 4.23 6.75 1 4.23 6.75 1 4.23 6.75
1
107.53 72.00 67.33 66.47 58.89 105.20 82.00 78.00 70.77 107.75 98.20 109.32 101.80 119.67 76.60 117.33
Bylaite Bylaite Bylaite Bylaite
40 Heilig 40 Heilig 30 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40
Martuscelli Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig
2008
* * * * * * * * * * * * * * *
45 ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
60 60 60 60 30 60 60 60
80.1 80.1 80.1 88.2 80.2 86.3 86.3 78.7
6
12.0 12.0 6 12.0 6 4.0 1 3.0 6 4.0 6 4.0 6 4.0
0.0 0.0 0.0 2.0 2.7 4.0 4.0 12.0
4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0
0.00 0.00 0.00 0.00 5 4.01 0.00 0.00 0.00
6
1
Page 46 of 66
6.4 6.4 1 6.4 1 5.0 3 9.3 1 4.9 1 4.9 1 4.5
1.51 1.51 1.51 0.78 0.67 0.77 0.77 0.74
92 3 3 3 99 3 92 3
1
1
4.23 6.75 4.23 6.75 7.00 6.75 1 4.23 6.75 1
2-Octanone 111-13-7
2.37-2.50 (2.44)
128 174 1.80
20 20 20 20
100.0 100.0 100.0 100.0
0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a.
0.00 0.00 0.00 0.00
0.0 0.0 0.0 0.0
0.00 0.00 0.00 0.00
n.a. n.a. n.a. n.a.
7.00 7.00 7.00 7.00
Heptanal 111-71-7
2.29-2.50 (2.37)
114 153 5.12
20 33 20 20 20
100.0 100.0 89.6 89.6 89.6
0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a. n.a.
0.00 0.00 2 0.40 2 0.40 2 0.40
0.0 0.0 2 10.0 2 10.0 2 10.0
0.00 0.00 0.00 1 0.02 1 0.04
n.a. n.a. n.a. n.a. n.a.
7.00 7.00 7.00 7.00 7.00
Methyl cinnamate 103-26-4
2.18 (2.18)
162 261 0.01
50
100.0
0.0
0.0
n.a.
0.00
0.0
0.00
n.a.
7.00
Ethyl-3Methylbutanoate 108-64-5
2.12-2.19 (2.16)
130 132 10.44
60
82.8
1
3.2
0.0
4.0
5
0.68
99
7.00
80.2
1
4.0
5
Ethyl-2-methylbutanoate 7452-79-1
2.10-2.12 (2.11)
130 133 10.44
60 100 100
95.2 95.2
100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
90.1 90.1 90.1 90.1 88.5 87.6 85.1 85.1 85.1 85.1 82.6 82.6 81.8 81.8 80.1 80.1 80.1 80.1
100 100 100 100
88.2 86.3 86.3 78.7
3.0
2.7
4.01
3
4.01
3
9.4 9.3
0.0 0.0
0.0 0.0
n.a. n.a.
0.00 0.00
1
4.0 4.0 6 4.0 6 4.0 6 6.0 6 6.0 6 8.0 6 8.0 6 8.0 6 8.0 6 12.0 6 12.0 6 12.0 6 12.0 6 12.0 6 12.0 6 12.0 6 12.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4.0 4.0 4.0 4.0 0.7 4.0 4.0 4.0 4.0 4.0 0.3 0.3 0.7 0.7 4.0 4.0 4.0 4.0
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
1
2.0 4.0 4.0 12.0
4.0 4.0 4.0 4.0
0.00 0.00 0.00 0.00
6 6
6
4.0 4.0 6 4.0 6 4.0 6
4.4 4.4
0.67
99
0.43 0.43
n.a. n.a.
5.1 5.1 5.1 1 5.1 1 4.8 1 5.4 1 5.7 1 5.7 1 5.7 1 5.7 1 4.6 1 4.6 1 5.2 1 5.2 1 6.4 1 6.4 1 6.4 1 6.4
0.79 0.79 0.79 0.79 0.75 0.97 1.15 1.15 1.15 1.15 0.85 0.85 1.07 1.07 1.51 1.51 1.51 1.51
92 3 3 92 92 3 92 92 3 3 3 3 3 3 92 92 3 3
1
0.78 0.77 0.77 0.74
3 3 92 3
1
1 1
5.0 4.9 4.9 1 4.5 1 1
7.00
125.95 121.73 103.00 291.50 1 781.25 569.80 479.50 1137.57
40 40 40 40 30 40 40 40
Heilig Heilig Heilig Heilig Martuscelli Heilig Heilig Heilig
200824 * * *
25 60 70 80
Jouquand Jouquand Jouquand Jouquand
25.38 34.13 1 22.99 1 22.47 1 27.86
37 40 37 37 37
Bylaite Benjamin Bylaite Bylaite Bylaite
409.84
1
161.29 1 13.40 1 8.33 1 5.41 1 1
1
1
30.21
1
144.93
* * * *
226 227 228 229 230 231 232 233
2004 200452 200452 200452
52
234 235 236 237
200325 201137 200325 25 2003 25 2003
238 239 240 241 242
25 Atlan
200614
243
30 Martuscelli
200824
244
30 Martuscelli
24
245
2008
1
4.23 6.75
15.75 12.43
40 Heilig 40 Heilig
* *
246 247
6.75 6.75 4.23 1 4.23 6.75 6.75 6.75 1 4.23 6.75 1 4.23 6.75 1 4.23 6.75 1 4.23 6.75 1 4.23 6.75 1 4.23
22.00 14.45 17.24 25.07 32.34 18.95 27.33 29.93 22.33 15.40 23.35 33.23 30.89 30.00 38.82 60.22 26.62 25.67
40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40
Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig
* * * * * * * * * * * * * * * * * *
248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265
6.75 6.75 4.23 6.75
58.00 114.98 139.37 293.63
40 40 40 40
Heilig Heilig Heilig Heilig
* * * *
266 267 268 269
1
1
46 ACS Paragon Plus Environment
Page 47 of 66
Journal of Agricultural and Food Chemistry
E,E-2,4-octadienal 30361-28-5
2.03-2.12 (2.08)
124 199 0.48
40 40 40 40
100.0 89.6 89.6 89.6
0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a.
E-2-heptenal 18829-55-5
1.92-2.11 (2.02)
112 166 2.42
40 40 40 40
100.0 89.6 89.6 89.6
0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a.
2-Heptanone 110-43-0
1.70-2.20 (1.96)
114 150 6.29
3 9 20 20 20 20 9 6 9
100.0 100.0 100.0 100.0 100.0 100.0 99.6 99.0 98.8
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
9 9 9 3 3 3 3
86.4 86.3 86.2 85.1 84.6 84.3 83.6
4.0 4.0 2 4.0 6 10.0 6 10.0 6 10.0 6 10.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
4.0 4.0 4.0 3.0 3.0 3.0 3.0
6 6
63.0 63.0
5
1 7.4 14.8
100.0 100.0
3.2
0.0
4.0
2 2
5
24.4 18.3 1
1
0.0 10.0 2 10.0 2 10.0
0.00 0.00 1 0.02 1 0.04
n.a. n.a. n.a. n.a.
7.00 7.00 7.00 7.00
1
270.27 526.32 1 833.33 1 312.50
37 37 37 37
Bylaite Bylaite Bylaite Bylaite
200325 25 2003 200325 200325
270 271 272 273
0.0 10.0 2 10.0 2 10.0
0.00 0.00 1 0.02 1 0.04
n.a. n.a. n.a. n.a.
7.00 7.00 7.00 7.00
1
116.28 153.85 1 106.38 1 104.17
37 37 37 37
Bylaite Bylaite Bylaite Bylaite
200325 25 2003 200325 200325
274 275 276 277
0.00 0.00 0.00 0.00 0.00 0.00 2 0.10 1 1.00 2 0.80
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 0.2
0.00 0.00 0.00 0.00 0.00 0.00 3 0.62 0.00 3 0.62
n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
7.00 6.50 7.00 7.00 7.00 7.00 3.50 7.00 3.50
1 83.33 105.26 1 175.44 1 25.64 1 14.01 1 9.71 100.00 1 609.76 83.33
30 30 25 60 70 80 30 10 30
Gierczynski Merabtine Jouquand Jouquand Jouquand Jouquand Merabtine Lauverjat Merabtine
200730 201029 200452 200452 52 2004 52 2004 29 2010 5 2009 29 2010
278 279 280 281 282 283 284 285 286
0.00 0.05 2 0.10 0.00 0.00 0.00 0.00
1
5.7 5.8 3 5.8 1 17.7 1 17.7 1 17.7 1 17.7
0.86 0.86 0.86 2.14 2 3.14 2 3.14 2 3.14
85 85 85 65 65 65 65
1
105.26 100.00 90.91 1 66.67 1 90.91 1 100.00 1 71.43
30 30 30 30 30 30 30
Merabtine Merabtine Merabtine Gierczynski Gierczynski Gierczynski Gierczynski
201029 201029 201029 200730 200730 200730 200730
287 288 289 290 291 292 293
1
2
n.a. n.a.
1
2
4.11 3.46
3
1
3
1
1020.41 1666.67
13 Lauverjat 13 Lauverjat
20095 20095
294 295
9.4
0.68
99
7.00
1
30 Martuscelli
200824
296
1
60 Jouquand 70 Jouquand 80 Jouquand
200452 200452 200452
297 298 299
30 25 60 70 80 37 30 37 37 37 30 30 30 30 30
200647 52 2004 200452 200452 200452 200325 200647 200325 200325 200325 200647 200647 200647 47 2006 47 2006
300 301 302 303 304 305 306 307 308 309 310 311 312 313 314
0.00 0.40 2 0.40 2 0.40 2
0.00 0.40 2 0.40 2 0.40 2
2
0.00 0.00 5
4.01
2
2
3
33.4 25.1 3
1
1
4.00 4.00 1 4.00 6.75 4 4.60 4 4.60 4 4.60 1
6.20 6.20
Benzyl acetate 140-11-4
1.96-1.97 (1.97)
150 214 0.24
24
82.8
1-Hexanol 111-27-3
1.86-2.03 (1.95)
102 157 1.23
20 20 20
100.0 100.0 100.0
0.0 0.0 0.0
0.0 0.0 0.0
n.a. n.a. n.a.
0.00 0.00 0.00
0.0 0.0 0.0
0.00 0.00 0.00
n.a. n.a. n.a.
7.00 7.00 7.00
Hexanal 66-25-1
1.78-1.97 (1.85)
100 131 14.50
7 20 20 20 20 20 7 20 20 20 7 7 7 7 7
100.0 100.0 100.0 100.0 100.0 100.0 98.6 89.6 89.6 89.6 64.8 63.4 63.1 63.0 62.6
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
0.00 0.00 0.00 0.00 0.00 0.00 4 1.40 2 0.40 2 0.40 2 0.40 0.00 5 1.45 5 1.73 6 1.80 5 2.20
0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 10.0 2 10.0 2 10.0 2 35.0 2 35.0 2 35.0 2 35.0 2 35.0
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1 0.02 1 0.04 0.19 0.19 0.19 0.19 0.19
n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00
719.42
117.65 1 75.19 1 49.50 1
86.21 90.91 1 12.00 1 7.87 1 5.29 1 38.91 1 95.24 1 37.88 1 36.50 1 40.16 1 84.66 1 102.56 1 102.56 1 102.56 1 104.58 1
Savary Jouquand Jouquand Jouquand Jouquand Bylaite Savary Bylaite Bylaite Bylaite Savary Savary Savary Savary Savary
47 ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
82.8
1
50 1 1 1
80.3 77.5 77.5 77.5
1
1 50 188 22 22 22 22 35 80 80 188 188 188
100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 95.2 95.2 63.1 63.1 63.1
540 80 80 80 80 80 80 80 80 80 80 1 1 1 1 80 80 80 80 80 80 80 80
82.8 90.1 90.1 90.1 90.1 88.5 87.6 85.1 85.1 85.1 85.1 85.1 84.6 84.3 83.6 82.6 82.6 81.8 81.8 80.1 80.1 80.1 80.1
80 540 80 80 27 27 27 80
88.2 80.2 86.3 86.3 77.5 77.5 77.5 78.7
10 10
100.0 99.6
12
Ethyl butanoate 105-54-4
Ethyl crotonate 623-70-1
1.70-1.85 (1.78)
1.60-1.85 (1.73)
116 121 17.02
114 143
4.0
5
2.7 4.0 4.0 1 4.0
4.0 4.5 4.0 2.1
5
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
3.2 4.0 6 4.0 6 4.0 6 4.0 6 6.0 6 6.0 6 8.0 6 8.0 6 8.0 6 8.0 6 10.0 6 10.0 6 10.0 6 10.0 6 12.0 6 12.0 6 12.0 6 12.0 6 12.0 6 12.0 6 12.0 6 12.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4.0 4.0 4.0 4.0 4.0 0.7 4.0 4.0 4.0 4.0 4.0 3.0 3.0 3.0 3.0 0.3 0.3 0.7 0.7 4.0 4.0 4.0 4.0
4.0 3.0 6 4.0 6 4.0 4 5.4 2 5.4 3 5.4 6 4.0
2.0 2.7 4.0 4.0 1 4.0 1 4.0 1 4.0 12.0
4.0 4.0 4.0 4.0 4.5 4.0 2.1 4.0
0.0 0.0
0.0 0.0
n.a. n.a.
3.2
3.0 5.4 5.4 3 5.4
0.0
4
1
2
1
1 6
6 1
4.01
3
4.01 0.00 0.00 0.00
3
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5 1.73 5 1.73 5 1.73 5
4.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
9.4
99
1
7.00
167.79 2336.45 1077.59 1 1137.66
0.67 1.20 1.20 1.20
99 80 80 80
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 4.4 1 4.4 2 35.0 2 35.0 2 35.0
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.43 0.43 0.19 0.19 0.19
n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 1 4.23 6.75 7.00 7.00 7.00
1
0.68 0.79 0.79 0.79 0.79 0.75 0.97 1.15 1.15 1.15 1.15 2.14 2 3.14 2 3.14 2 3.14 0.85 0.85 1.07 1.07 1.51 1.51 1.51 1.51
99 92 92 3 3 92 3 92 92 3 3 65 65 65 65 3 3 3 3 92 92 3 3
7.00 6.75 1 4.23 6.75 1 4.23 6.75 6.75 6.75 1 4.23 6.75 1 4.23 6.75 4 4.60 4 4.60 4 4.60 6.75 1 4.23 6.75 1 4.23 6.75 1 4.23 6.75 1 4.23
0.78 0.67 0.77 0.77 1.20 1.20 1.20 0.74
3 99 3 92 80 80 80 3
6.75 7.00 6.75 1 4.23 2 4.60 2 4.60 2 4.60 6.75
0.00 0.62
n.a. n.a.
6.50 3.50
3
9.4 5.1 1 5.1 1 5.1 1 5.1 1 4.8 1 5.4 1 5.7 1 5.7 1 5.7 1 5.7 1 17.7 1 17.7 1 17.7 1 17.7 1 4.6 1 4.6 1 5.2 1 5.2 1 6.4 1 6.4 1 6.4 1 6.4 1
1
0.00 0.10
0.0 0.0
3 1
3
7.00 4.60 4.60 2 4.60
40.65
1
9.3 11.8 11.8 3 11.8
5.0 9.3 4.9 1 4.9 3 11.8 3 11.8 3 11.8 1 4.5
2
0.68
3
3
0.00 4.01 0.00 0.00 0.00 0.00 0.00 0.00
5
Page 48 of 66
2
1
2
1
30 Martuscelli
200824
315
24
30 4 4 4
Martuscelli Saint-Eve** Saint-Eve** Saint-Eve**
2008 200645 200645 200645
316 317 318 319
41.67 43.29 1 54.64 1 55.56 1 12.50 1 8.06 1 5.99 1 56.37 28.75 22.09 1 89.69 1 63.16 1 53.48
30 25 30 25 60 70 80 40 40 40 10 20 30
Gierczynski Atlan Savary Jouquand Jouquand Jouquand Jouquand Benjamin Heilig Heilig Savary Savary Savary
2007 200614 200647 200452 200452 200452 52 2004 37 2011 * * 47 2006 47 2006 47 2006
30
320 321 322 323 324 325 326 327 328 329 330 331 332
1
43.86 34.67 36.67 30.46 27.68 46.19 29.50 36.67 37.70 36.25 30.40 1 40.00 1 43.48 1 42.55 1 35.71 43.89 56.00 47.49 47.13 44.94 66.92 35.47 35.00
30 40 40 40 40 40 40 40 40 40 40 30 30 30 30 40 40 40 40 40 40 40 40
Martuscelli Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Gierczynski Gierczynski Gierczynski Gierczynski Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig
200824 * * * * * * * * * * 30 2007 30 2007 200730 200730 * * * * * * * *
333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355
53.00 107.76 97.40 106.35 1 483.09 1 458.72 1 389.11 206.00
40 30 40 40 4 4 4 40
Heilig Martuscelli Heilig Heilig Saint-Eve** Saint-Eve** Saint-Eve** Heilig
*
2006 45 2006 45 2006 *
45
356 357 358 359 360 361 362 363
201029 201029
364 365
1
1
117.65 133.33
30 Merabtine 30 Merabtine
2008
24
* *
48 ACS Paragon Plus Environment
Page 49 of 66
Ethylbutanal 97-96-1
Journal of Agricultural and Food Chemistry
1.70-1.73 (1.72)
9.14
10
98.8
0.0
0.0
n.a.
86.4 86.3 86.2 100.0 99.6 98.8
2
100 117 22.48
10 10 10 8 8 8
4.0 4.0 2 4.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0
4.0 4.0 4.0 n.a. n.a. n.a.
8 8 8
86.4 86.3 86.2
2
4.0 4.0 4.0
0.0 0.0 0.0
4.0 4.0 4.0
2
2 2
t-2-hexenal 85761-70-2
1.58-1.79 (1.69)
98 142 8.78
n.a. n.a. n.a.
100.0 100.0 100.0
0.0 0.0 0.0
0.0 0.0 0.0
n.a. n.a. n.a.
2-methyl-pentanal 123-15-9
1.79 (1.79)
100 120 22.48
20 20 20 20
100.0 89.6 89.6 89.6
0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a.
Hexenal 6728-26-3
1.40-1.79 (1.60)
98 147 6.15
8 40 8 8 40 40 40
100.0 100.0 99.6 98.8 89.6 89.6 89.6
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a. n.a. n.a. n.a.
8 8 8
86.4 86.3 86.2
4.0 4.0 2 4.0
0.0 0.0 0.0
4.0 4.0 4.0
50 163 100 100
100.0 100.0 95.2 95.2
0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a.
100 100 100 100 100 100 100 100 100 100
90.1 90.1 90.1 90.1 85.1 85.1 85.1 81.8 80.1 80.1
4.0 4.0 6 4.0 6 4.0 6 8.0 6 8.0 6 8.0 6 12.0 6 12.0 6 12.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4.0 4.0 4.0 4.0 4.0 4.0 4.0 0.7 4.0 4.0
100 750 100 24 24
88.2 80.3 86.3 77.5 77.5
2.0 2.7 4.0 1 4.0 1 4.0
4.0 4.0 4.0 4.5 4.0
(Z)-3-hexenol 928-96-1
1.56-1.61 (1.59)
100 157 1.38
2 2
6 6
6
4.0 3.0 6 4.0 4 5.4 2 5.4 1
2
0.80
2
3
0.62
n.a.
0.00 0.05 2 0.10 0.00 2 0.10 2 0.80
1
0.86 0.86 0.86 0.00 3 0.62 3 0.62
85 85 85 n.a. n.a. n.a.
1
0.00 0.05 0.10
1
5.7 5.8 5.8
0.86 0.86 0.86
85 85 85
1
0.0 0.0 0.0
0.00 0.00 0.00
n.a. n.a. n.a.
7.00 7.00 7.00
1
0.2
5.7 5.8 3 5.8 0.0 0.0 2 0.2
2
3
2
3
2
3
0.00 0.00 0.00 0.00 0.40 2 0.40 2 0.40
7.00 7.00 7.00 7.00
0.00 0.00 2 0.10 2 0.80 2 0.40 2 0.40 2 0.40
0.0 0.0 0.0 2 0.2 2 10.0 2 10.0 2 10.0
0.00 0.00 3 0.62 3 0.62 0.00 1 0.02 1 0.04
n.a. n.a. n.a. n.a. n.a. n.a. n.a.
6.50 7.00 3.50 3.50 7.00 7.00 7.00
5.7 5.8 3 5.8
0.86 0.86 0.86
85 85 85
0.00 0.00 0.00 0.00
0.0 0.0 1 4.4 1 4.4
0.00 0.00 0.43 0.43
n.a. n.a. n.a. n.a.
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
1
5.1 5.1 5.1 1 5.1 1 5.7 1 5.7 1 5.7 1 5.2 1 6.4 1 6.4
0.79 0.79 0.79 0.79 1.15 1.15 1.15 1.07 1.51 1.51
92 92 3 3 92 3 3 3 3 3
0.00 4.01 0.00 0.00 0.00
1
0.78 0.67 0.77 1.20 1.20
3 99 3 80 80
5
1
5.0 9.3 4.9 3 11.8 3 11.8 3 1
30 30 30 30 30 30
Merabtine Merabtine Merabtine Merabtine Merabtine Merabtine
201029 29 2010 201029 201029 201029 29 2010
367 368 369 370 371 372
25.64 22.86 23.53
30 Merabtine 30 Merabtine 30 Merabtine
201029 201029 29 2010
373 374 375
46.95 36.23 1 29.94
60 Jouquand 70 Jouquand 80 Jouquand
200452 200452 200452
376 377 378
1
22.78 21.79 1 20.70 1 22.52
37 37 37 37
Bylaite Bylaite Bylaite Bylaite
2003 200325 200325 25 2003
25
379 380 381 382
222.22 178.57 105.26 105.26 1 161.29 1 163.93 1 156.25
30 37 30 30 37 37 37
Merabtine Bylaite Merabtine Merabtine Bylaite Bylaite Bylaite
201029 200325 29 2010 201029 200325 200325 200325
383 384 385 386 387 388 389
30 Merabtine 30 Merabtine 30 Merabtine
201029 201029 201029
390 391 392
1841.62 1865.67 611.50 432.21
25 30 40 40
Atlan Savary Heilig Heilig
200614 47 2006 * *
393 394 395 396
797.67 708.67 523.75 520.33 765.67 1231.25 679.80 777.00 1040.21 554.67
40 40 40 40 40 40 40 40 40 40
Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig
* * * * * * * * * *
397 398 399 400 401 402 403 404 405 406
922.00 1754.39 891.50 1 5494.51 1 6622.52
40 30 40 4 4
Heilig Martuscelli Heilig Saint-Eve** Saint-Eve**
* 200824 * 200645 45 2006
407 408 409 410 411
4.00 4.00 4.00
n.a. n.a. n.a. n.a.
1
111.11 105.26 100.00 25.00 21.05 21.98
1
0.00 0.00 1 0.02 1 0.04
3
366
1
0.0 10.0 2 10.0 2 10.0
1
201029
4.00 4.00 1 4.00 6.50 3.50 3.50
2
0.00 0.05 2 0.10
30 Merabtine
1
2
2
142.86
3.50
1
1
1
1
500.00 454.55 408.16
4.00 4.00 1 4.00 1
7.00 7.00 1 4.23 6.75 6.75 4.23 6.75 1 4.23 1 4.23 6.75 1 4.23 1 4.23 6.75 1 4.23 1
6.75 7.00 6.75 2 4.60 2 4.60
1 1
1
49 ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
24 100
77.5 78.7
3 6
5.4 4.0
1 4.0 12.0
2.1 4.0
E,E-2,4-heptadienal 4313-03-5
1.51-1.59 (1.55)
110 n.a. 1.38
40 40 40 40
100.0 89.6 89.6 89.6
0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a.
2-Hexanone 591-78-6
1.38 (1.38)
100 128 15.43
n.a. n.a. n.a.
100.0 100.0 100.0
0.0 0.0 0.0
0.0 0.0 0.0
n.a. n.a. n.a.
Pentanal 110-62-3
1.29-1.44 (1.37)
86 103 42.29
20 20 20 20
100.0 89.6 89.6 89.6
0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a.
2-methyl butanal 96-17-3
1.25-1.34 (1.30)
86 92 65.57
20 20 20 20
100.0 89.6 89.6 89.6
0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a.
3-methyl butanal 590-86-3
1.25-1.34 (1.30)
86 92 65.57
20 20 20 20
100.0 89.6 89.6 89.6
0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a.
Methylpentenone 141-79-7
1.12-1.30 (1.21)
98 130 11.65
8 8 8
100.0 99.6 98.8
0.0 0.0 0.0
0.0 0.0 0.0
n.a. n.a. n.a.
8 8 8
86.4 86.3 86.2
4.0 4.0 2 4.0
0.0 0.0 0.0
4.0 4.0 4.0
2 2
0.00 0.00 0.00 0.40 2 0.40 2 0.40 2
3
0.00 0.40 2 0.40 2 0.40 0.00 0.40 2 0.40 2 0.40 2
0.00 0.40 2 0.40 2 0.40 2
2
1
4 Saint-Eve** 40 Heilig
200645 *
412 413
37 37 37 37
Bylaite Bylaite Bylaite Bylaite
200325 25 2003 200325 200325
414 415 416 417
34.48 22.99 1 16.69
60 Jouquand 70 Jouquand 80 Jouquand
200452 200452 200452
418 419 420
7.00 7.00 7.00 7.00
1
59.17 62.89 1 68.49 1 62.11
37 37 37 37
Bylaite Bylaite Bylaite Bylaite
2003 200325 200325 200325
25
421 422 423 424
n.a. n.a. n.a. n.a.
7.00 7.00 7.00 7.00
1
31.65 31.25 1 30.40 1 32.57
37 37 37 37
Bylaite Bylaite Bylaite Bylaite
2003 200325 200325 200325
25
425 426 427 428
0.00 0.00 1 0.02 1 0.04
n.a. n.a. n.a. n.a.
7.00 7.00 7.00 7.00
1
38.91 37.88 1 36.90 1 39.37
37 37 37 37
Bylaite Bylaite Bylaite Bylaite
200325 200325 200325 25 2003
429 430 431 432
0.00 0.62 3 0.62
n.a. n.a. n.a.
6.50 3.50 3.50
266.67 250.00 243.90
30 Merabtine 30 Merabtine 30 Merabtine
201029 201029 29 2010
433 434 435
5.7 5.8 3 5.8
0.86 0.86 0.86
85 85 85
222.22 200.00 181.82
30 Merabtine 30 Merabtine 30 Merabtine
201029 201029 201029
436 437 438
25 Atlan
200614
439
20 Tehrany
200769
440
1.20 0.74
80 3
0.0 10.0 2 10.0 2 10.0
0.00 0.00 1 0.02 1 0.04
n.a. n.a. n.a. n.a.
7.00 7.00 7.00 7.00
0.0 0.0 0.0
0.0 0.0 0.0
n.a. n.a. n.a.
7.00 7.00 7.00
1
0.0 10.0 2 10.0 2 10.0
0.00 0.00 1 0.02 1 0.04
n.a. n.a. n.a. n.a.
0.0 10.0 2 10.0 2 10.0
0.00 0.00 1 0.02 1 0.04
0.0 10.0 2 10.0 2 10.0
2
2
2
2
0.00 0.10 2 0.80
0.0 0.0 2 0.2
0.00 0.05 2 0.10
1
2
2
3
3
4.60 6.75
9803.92 862.25
11.8 1 4.5
0.00 0.00 0.00 2
Page 50 of 66
1
434.78 400.00 1 666.67 1 312.50 1
1
1
1
1
1
4.00 4.00 1 4.00 1
1
3194.89
Vanillin 121-33-5
1.19-1.21 (1.20)
152 286 0.00
50
100.0
0.0
0.0
n.a.
0.00
0.0
0.00
n.a.
7.00
Isopropyl acetate 108-21-4
1.06-1.20 (1.13)
102 89 80.29
87
100.0
0.0
0.0
n.a.
0.00
0.0
0.00
n.a.
7.00
E-2-pentanal 1576-87-0
0.88-1.28 (1.08)
84 n.a. 15.23
40 40 40 40
100.0 89.6 89.6 89.6
0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a.
0.0 10.0 2 10.0 2 10.0
0.00 0.00 1 0.02 1 0.04
n.a. n.a. n.a. n.a.
7.00 7.00 7.00 7.00
1
263.16 238.10 1 238.10 1 232.56
37 37 37 37
Bylaite Bylaite Bylaite Bylaite
200325 200325 200325 25 2003
441 442 443 444
E,E-2,4-hexadienal 142-83-6
0.99-1.06 (1.03)
96 174 3.95
40 40 40 40
100.0 89.6 89.6 89.6
0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a.
0.0 10.0 2 10.0 2 10.0
0.00 0.00 1 0.02 1 0.04
n.a. n.a. n.a. n.a.
7.00 7.00 7.00 7.00
1
37 37 37 37
Bylaite Bylaite Bylaite Bylaite
200325 25 2003 200325 200325
445 446 447 448
Butanal
0.81-0.88
72
80
100.0
0.0
0.0
n.a.
0.0
0.00
n.a.
7.00
200769
449
0.00 0.40 2 0.40 2 0.40 2
0.00 0.40 2 0.40 2 0.40 2
0.00
2
2
41.10
1
555.56 625.00 1 625.00 1 526.32 1
40.90
20 Tehrany
50 ACS Paragon Plus Environment
Page 51 of 66
123-72-8
Journal of Agricultural and Food Chemistry
(0.84)
75 n.a.
2-methyl propanal 78-84-2
0.74-0.82 (0.78)
72 63 230.09
Ethyl acetate 141-78-6
0.71-0.73 (0.72)
88 77 88.05
20 20 20 20 20 20 20 20
100.0 89.6 89.6 89.6 100.0 89.6 89.6 89.6
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
50 123 90 1000
123 123 123
100.0 100.0 100.0 99.0 99.0 64.0 63.1 63.1 63.1
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1000
81.8
2
5.4
0.0
1000 1000 18 18 18 1000 1000 1000 1000 1000 1000 1000
89.6 79.6 77.5 77.5 77.5 77.5 77.5 77.5 77.5 77.5 77.5 77.5
1
4.4 5.4 5.4 2 5.4 3 5.4 2 5.4 2 5.4 4 5.4 4 5.4 3 5.4 3 5.4 2 5.4
0.00 0.40 2 0.40 2 0.40 0.00 2 0.40 2 0.40 2 0.40
450 451 452 453 454 455 456 457
103.84 1 61.73 37.70 1 361.01 1 81.97 1 86.21 1 160.00 1 75.02 1 61.54
25 30 20 7 25 25 10 20 30
Atlan Savary Tehrany Deleris** Deleris** Deleris** Savary Savary Savary
200614 200647 69 2007 46 2007 200848 200848 200647 200647 200647
458 459 460 461 462 463 464 465 466
8 Deleris**
200936
467
46
7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00
n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
0.00 0.00 0.00 1 1.00 1 1.00 1 1.00 5 1.73 5 1.73 5 1.73
0.0 0.0 0.0 0.0 0.0 2 35.0 2 35.0 2 35.0 2 35.0
0.00 0.00 0.00 0.00 0.00 0.00 0.19 0.19 0.19
n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00 7.00
4.0
0.00
3
1.16
80
2
1
2
1
2
1
2
1
11.6 1
5.0 11.8 11.8 3 11.8 3 11.8 3 12.5 3 12.5 3 12.5 3 12.5 3 12.5 3 12.5 3 11.9
0.94 1.18 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20
90 80 80 80 80 80 80 80 80 80 80 80
3 3
1
1
354.61
4.60
4.00 4.60 4.60 2 4.60 2 4.60 2 4.60 2 4.60 2 4.60 2 4.60 2 4.60 2 4.60 2 4.60
0.26-0.47 (0.34)
72 79.3 120.50
100 250 250 250
100.0 100.0 100.0 100.0
0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a.
0.00 0.00 0.00 0.00
0.0 0.0 0.0 0.0
0.00 0.00 0.00 0.00
n.a. n.a. n.a. n.a.
7.00 7.00 7.00 7.00
1-propanol 71-23-8
0.25 (0.25)
60 98 27.93
32
100.0
0.0
0.0
n.a.
0.00
0.0
0.00
n.a.
7.00
Acetaldehyde 75-07-0
(-0.22)-(-0.16) 44 (-0.19) 20 1199.7
78
100.0
0.0
0.0
n.a.
0.00
0.0
0.00
n.a.
7.00
Acetonitrile 75-05-8
(-0.39)-(-0.34) 41 (-0.37) 82 118.10
79
100.0
0.0
0.0
n.a.
0.00
0.0
0.00
n.a.
7.00
Diacetyl 431-03-8
(-1.80)-(1.30) (-1.48)
50 30 1000
100.0 100.0 99.0 99.0 98.6
0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a. n.a.
0.00 0.00 1 1.00 1 1.00 4 1.40
0.0 0.0 0.0 0.0 0.0
0.00 0.00 0.00 0.00 0.00
n.a. n.a. n.a. n.a. n.a.
7.00 7.00 7.00 7.00 7.00
30
200325 25 2003 25 2003 25 2003 200325 200325 25 2003 200325
n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
2-Butanone 78-93-3
86 88 75.54
Bylaite Bylaite Bylaite Bylaite Bylaite Bylaite Bylaite Bylaite
0.00 0.00 1 0.02 1 0.04 0.00 0.00 1 0.02 1 0.04
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
1
37 37 37 37 37 37 37 37
0.0 10.0 2 10.0 2 10.0 0.0 2 10.0 2 10.0 2 10.0
4.0 4.0 4.5 4.0 2.1 4.0 4.0 5.2 5.2 2.0 2.0 4.0
1
4
78.74 60.61 1 57.14 1 60.98 1 44.84 1 35.09 1 30.86 1 32.47
2
0.1 2.0 4.0 1 4.0 1 4.0 1 4.0 1 4.0 1 4.0 1 4.0 1 4.0 1 4.0 1 4.0
2
1
2
403.23 312.50 483.09 1 483.09 1 436.68 1 257.07 1 273.22 1 268.10 1 258.40 1 253.81 1 274.73 1 347.22
7 8 4 4 4 7 7 7 7 7 7 8
34.80 100.00 1 61.35 1 42.37
20 60 70 80
1
1
1408.45
1
Deleris** Deleris** Saint-Eve** Saint-Eve** Saint-Eve** Deleris** Deleris** Deleris** Deleris** Deleris** Deleris** Deleris**
2007 200936 200645 200645 200645 200746 200746 200746 200746 200746 200746 200936
468 469 470 471 472 473 474 475 476 477 478 479
Tehrany Jouquand Jouquand Jouquand
2007 200452 200452 200452
69
480 481 482 483 483 484
37
40 Benjamin
2011
39.90
20 Tehrany
200769
485
42.40
20 Tehrany
200769
486
25 30 7 25 30
200614 200647 46 2007 200848 200647
487 488 489 490 491
1501.50 1 79.37 1 4166.67 1 1136.36 1 888.89
Atlan Savary Deleris** Deleris** Savary
51 ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
a
120 120 30 30 30 30 30
95.2 95.2 64.8 63.4 63.1 63.0 62.6
120 120 120 120 120 120 120 120 200 200 200 200 120 1000 120 120 120 120 120 120 120 120 120 120 120
91.2 91.0 90.7 90.4 90.1 90.1 90.1 90.1 90.1 90.1 90.1 90.1 89.9 81.8 88.5 87.6 85.1 85.1 85.1 82.6 82.6 81.8 80.1 80.1 80.1
1000 200 200 120 1000 120 200 200 200 4 4 4 1000 1000 1000 1000 1000 1000 1000
89.6 89.7 89.2 88.2 79.6 86.3 86.3 86.3 86.3 77.5 77.5 77.5 77.5 77.5 77.5 77.5 77.5 77.5 77.5
0.0 0.0 0.0 0.0 0.0 0.0 0.0 6
4.0 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 6 4.0 2 5.4 6 6.0 6 6.0 6 8.0 6 8.0 6 8.0 6 12.0 6 12.0 6 12.0 6 12.0 6 12.0 6 12.0 6
1
4.4 4.0 4.0 6 4.0 2 5.4 6 4.0 6 4.0 6 4.0 6 4.0 4 5.4 2 5.4 3 5.4 2 5.4 2 5.4 4 5.4 4 5.4 3 5.4 3 5.4 2 5.4 6 6
0.0 0.0 0.0 0.0 0.0 0.0 0.0
n.a. n.a. n.a. n.a. n.a. n.a. n.a.
0.00 0.00 0.00 5 1.45 5 1.73 6 1.80 5 2.20
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.3 0.7 1.5 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 100.0 4.0 0.7 4.0 4.0 4.0 4.0 0.3 0.3 0.7 4.0 4.0 4.0
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2 0.05 2 0.10 2 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.1 0.5 1.0 2.0 1 2.0 4.0 4.0 4.0 4.0 1 4.0 1 4.0 1 4.0 1 4.0 1 4.0 1 4.0 1 4.0 1 4.0 1 4.0 1 4.0
4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.5 4.0 2.1 4.0 4.0 5.2 5.2 2.0 2.0 4.0
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
1
4.4 4.4 2 35.0 2 35.0 2 35.0 2 35.0 2 35.0 1
1
4.3 4.5 1 4.7 1 4.9 1 5.1 1 5.1 1 5.1 1 5.1 1 5.1 1 5.1 1 5.1 1 5.1 1 5.3 3 11.6 1 4.8 1 5.4 1 5.7 1 5.7 1 5.7 1 4.6 1 4.6 1 5.2 1 6.4 1 6.4 1 6.4 1
1
5.0 5.0 5.0 1 5.0 3 11.8 1 4.9 1 4.9 1 4.9 1 4.9 3 11.8 3 11.8 3 11.8 3 12.5 3 12.5 3 12.5 3 12.5 3 12.5 3 12.5 3 11.9 1 1
Page 52 of 66
1
4.23 6.75 7.00 7.00 7.00 7.00 7.00
512.00 515.74 761.90 1 1000.00 1 1000.00 1 888.89 1 888.89
40 40 30 30 30 30 30
Heilig Heilig Savary Savary Savary Savary Savary
2006 200647 200647 200647 200647
492 493 494 495 496 497 498
3 3 3 3 3 3 92 92 99 3 3 57 3 80 92 3 3 3 92 3 3 3 3 3 92
6.75 6.75 6.75 6.75 6.75 1 4.23 6.75 1 4.23 6.75 6.75 6.75 6.75 6.75 4.60 6.75 6.75 6.75 1 4.23 1 4.23 6.75 1 4.23 1 4.23 6.75 1 4.23 1 4.23
647.33 563.86 1468.50 756.33 696.27 445.88 668.00 476.33 892.67 662.40 533.25 702.28 973.33 1 3225.81 1452.25 1281.00 2091.33 524.00 583.50 1310.00 466.83 416.73 6337.00 638.67 446.00
40 40 40 40 40 40 40 40 40 40 40 40 40 8 40 40 40 40 40 40 40 40 40 40 40
Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Deleris** Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig Heilig
201115 201115 15 2011 15 2011 15 2011 15 2011 * * * * * * 15 2011 36 2009 * * 15 2011 201115 * * * * 15 2011 201115 *
499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523
90 3 3 3 80 92 57 92 99 80 80 80 80 80 80 80 80 80 80
1
1
7 40 40 40 8 40 40 40 40 4 4 4 7 7 7 7 7 7 8
Deleris** Heilig Heilig Heilig Deleris** Heilig Heilig Heilig Heilig Saint-Eve** Saint-Eve** Saint-Eve** Deleris** Deleris** Deleris** Deleris** Deleris** Deleris** Deleris**
200746 * * * 36 2009 * * * * 45 2006 200645 200645 200746 200746 200746 46 2007 46 2007 46 2007 36 2009
524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542
0.43 0.43 0.19 0.19 0.19 0.19 0.19
n.a. n.a. n.a. n.a. n.a. n.a. n.a.
0.49 0.57 0.64 0.72 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.86 1.16 0.75 0.97 1.15 1.15 1.15 0.85 0.85 1.07 1.51 1.51 1.51 0.94 0.79 0.78 0.78 1.18 0.77 0.77 0.77 0.77 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20
4.00 6.75 6.75 6.75 2 4.60 1 4.23 6.75 6.75 6.75 2 4.60 2 4.60 2 4.60 2 4.60 2 4.60 2 4.60 2 4.60 2 4.60 2 4.60 2 4.60
1
6250.00 581.97 583.80 505.74 1 2380.95 515.67 433.13 502.04 548.20 1 2392.34 1 1003.01 1 841.75 5555.56 3846.15 4545.45 3125.00 6666.67 2857.14 2941.18
* *
47
Trivial name and CAS-No. as given by the respective study’s authors; most likely CAS-No. was assigned if not given in the literature; molecular structure from http://www.sigmaaldrich.com/.
52 ACS Paragon Plus Environment
Page 53 of 66
b
Journal of Agricultural and Food Chemistry
range and mean of log P-values given in the reviewed studies, including log P-values from http://www.thegoodscentscompany.com/; M: molecular mass, BP: boiling point at 1013 hPa, pi0: saturated vapor pressure at 25 ° C, from
http://www.thegoodscentscompany.com/. c
(Dairy) matrix composition listing the ACC: aroma compound concentration, water content, protein content, milk fat content, CWR: casein to whey protein ratio, Thick.: thickener content, Disacc.: disaccharide content in the matrix.
d
(Dairy) matrix processing listing the DWPD: degree of whey protein denaturation, and pH.
e
Matrix/gas partition coefficient KMG, determined at the respective equilibration temperature to the right; KMG was reported in studies identified by the respective first author and the year of publication.
f
Protein originates from 1: fresh milk, 2: reconstituted skim milk powder, 3: reconstituted skim milk and whey protein concentrate powder, 4: reconstituted skim milk and sodium caseinate powder, 5: reconstituted from skim milk ultrafiltration
retentate powder, 6: reconstituted micellar casein and whey protein isolate powder g
Milk fat originates from fresh cream or 1: anhydrous milk fat.
h
If not specified in the literature, a casein to whey protein ratio CWR of 4 was assumed for dairy matrices made of fresh milk or skim milk powder, and 100 for skim milk ultrafiltration retentate powder.
i
Thickener content originates from 1: agar, 2: pectin, 3: carrageenan, 4: starch, 5: starch + carrageenan, 6: starch + pectin.
j
Disaccharide content originates from 1: lactose, 2: sucrose, 3: lactose + sucrose; if not specified in the literature, a protein to lactose ratio of 0.73 and 0.70 was assumed for (reconstituted) skim and whole milk, respectively .
k
Ash content includes added 1: CaCl2, 2: NaCl, 3: Na-citrate + Ca-citrate + K-sorbate; if not specified in the literature, a protein to ash ratio of 4.67 and 4.50 was assumed for (reconstituted) skim and whole milk, respectively .
l
19
19
If not specified in the literature, the degree of whey protein denaturation DWPD was estimated from whey protein denaturation kinetics and the heat treatment denoted in the literature .
m n
19
pH / gel set by 1: addition of glucono-δ-lactone, 2: microbial fermentation, 3: glucono-δ-lactone + rennet, 4: microbial fermentation + rennet. For studies that reported the 1: gas/matrix partition coefficient KGM, it was converted to the matrix/gas partition coefficient KMG via KMG = 1 / KGM.
* determined according to Heilig et al.15. ** KMG-values tabulated in the literature were corrected according to the respective authors’ manuscript specifications.
53 ACS Paragon Plus Environment
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Page 54 of 66
Table 3: Compilation of aroma compounds reported as being non-detectable in studies using the PRV-method to determine matrix/gas partition coefficients. Trivial name
log P7
CAS-no.
BP (°C)7
pi0 (hPa)7
Decanoic acid*
6
334-48-5
4.09
269
0.00
Ethyl octanoate*
106-32-16
3.90
207
6
3.28
196
Linalool*
78-70-6
6
Octanal*
124-13-0
2.95
1,2
γ-decalactone*
706-14-9
172
2.72
267
ACC (ppm)9 3
at °C
4
10, 21, 303; 44
0.30
14
44
0.12
2
8 ;1
4
4
4
30
5
30 ; 40 ; 10, 21, 30
3
5
0.04
2.75 1
0.01
2
12-100 ; 30 ; 17
3
1
2
3
5
305
3-25
1
301
1-5
1
301
3391-86-4
6
24851-98-7
1
93-92-5
1
2.28
214
0.27
Methyl cinnamate*
103-26-41; 1754-62-72
2.18
2611; 2552
0.01
14-1201; 302; 153; 24
301; 402; 10, 21, 303; 44
Methyl anthranilate
134-20-31
2.04
240
0.02
1-51
301
0.24
1-10
1
301
10
5
180 ; 8 ; 1 ; 25
5
1
5
305 301; 10, 21, 303; 44
Oct-1-en-3-ol Methyl dihydrojasmonate Methyl benzyl acetate
2.64 2.50
1
Benzyl acetate*
140-11-4
Phenylacetaldehyde
308
1.96
6
122-78-1
214
1.78
2
Hexanoic acid
175
142-62-1
194
1.72
203
0.71 0.00
0.49 2
0.21
3
4
3-methylbutanal*
6
590-86-3
1.25
92
65.57
Vanillin*
121-33-51
1.19
286
0.00
3-251; 1083; 16
2-methyl butyric acid
116-53-02
1.13
177
0.74
1802
6
Butanoic acid
107-92-6
Furaneol
3658-77-3
118-71-8
Diacetyl* 24
209
0.07
431-03-8
Martuscelli et al.
163
0.34
6
Maltol
1
0.79
1,2
285
-1.34 2
88 15
(custards with 0 to 2.7 % milk fat); Heilig et al.
aqueous solution with 0.05 to 1.4 % of added thickeners);
4
3
3
40 ; 10, 21, 30 ; 4 ; 305
1
2
3
10, 21, 30 ; 44
3
4
30 ; 40 ; 10, 21, 303; 44
3
224 ; 33
4
10, 21, 303; 44
24
5
3-25 ; 27 ; 127 ; 19
n.a. 75.54
3
1
2
30 47
(milk protein solutions with 0 to 4 % milk fat); Savary et al. 45
Saint-Eve et al.
(stirred yoghurts with 4 % milk fat);
6
5
(water and 30
Gierczynski et al.
(water
7
and model cheese with 10 % protein and 0 % milk fat); estimated as not specified in the literature; log P, BP: boiling point at 1013 hPa, pi0: saturated vapor pressure at 25 °C, from http://www.thegoodscentscompany.com/; 8 rounded values; ACC = aroma compound concentration in the analyzed matrix; n.a. = not available; * reported to be detectable by some authors (see Table 1).
54
ACS Paragon Plus Environment
4
402
4
15 ; 2
2.19 0.04
305 2
5
Page 55 of 66
Journal of Agricultural and Food Chemistry
Table 4: Aroma compounds that were reported to be either not / less or more retained by higher fat contents. Trivial name
log Pa
CAS-no.
Less retained / no change at higher fat contents Diacetyl -1,34 431-03-8 1-propanol +0,25 Furaneol +0,34 Ethyl acetate +0,71 Butyric acid +0,79 Pyridine +0,84 Z-3-hexenol +1,61 More retained at higher fat contents (E,E)-2,4-hexadienal +1,06 Guaiacol +1,34 Benzaldehyde +1,48 Ethyl isobutanoate +1,77 t-2-hexenal +1,79 (E)-2-hexenal +1,79 Ethyl butanoate +1,85 Hexanal +1,97 Ethyl-2-methylbutanoate +2,12 2,3-diethyl-5-methylpyrazine +2,16 Ethylguaiacol +2,18 Isoamyl acetate +2,26 Amyl acetate +2,30 Ethyl pentanoate +2,30 +2,42 δ-decalactone (Z)-3-hexenyl acetate +2,42 Heptanal +2,50 (E,E)-2,6-nonadienal +2,60 (E,Z)-2,6-nonadienal +2,60 2-isobutyl-3-methoxypyrazine +2,62 1-octen-3-ol +2,64 (E,E)-2,4-nonadienal +2,65 Ethyl hexanoate +2,83 Octanol +2,88 (Z)-6-Nonenal +3,11 (E)-2-nonenal +3,17 Linalool +3,28 Nonanal +3,46 2-decanone +3,73 Ethyl octanoate +3,90 2-pentylfuran +3,97 +4,04 β-damascenone Geranyl acetate +4,10 Limonene +4,57 a
First author
71-23-8 3658-77-3 141-78-6 107-92-6 110-86-1 928-96-1
Guyot1*; Miettinen2*; Haahr42*; Deleris36*; Leksrisompong3; Roberts38*; Roberts68*; Saint-Eve45* Benjamin37* 3 Leksrisompong Weel43*; Saint-Eve45* 1 54 36 Guyot *; Nongonierma *; Deleris * Roberts68* 15 Heilig
142-83-6 90-05-1 100-52-7 97-62-1 6728-26-3 6728-26-3 105-54-4 66-25-1 7452-79-1 18138-04-0 2785-89-9 123-92-2 628-63-7 539-82-2 705-86-2 3681-71-8 111-71-7 557-48-2 557-48-2 24683-00-9 3391-86-4 5910-87-2 123-66-0 111-87-5 2277-19-2 18829-56-6 78-70-6 124-19-6 693-54-9 106-32-1 3777-69-3 23696-85-7 105-87-3 138-86-3
Haahr42* Roberts68* Roberts38* Nongonierma54* Meynier31* Haahr36* Weel43*; Nongonierma54*; Benjamin37*; Heilig15; Roberts38* Haahr42*; Nongonierma54*; Roberts38*; Meynier31* Heilig15 Roberts68* Roberts68* Meynier31* Meynier31* Meynier31* Guyot1*; Leksrisompong3 Heilig15 Benjamin37* Haahr42* Haahr42* Roberts68* Roberts68* Haahr42* Weel43*; Nongonierma54*; Deleris36*; Heilig15 Benjamin37* Haahr42* Haahr42* Miettinen2*; Deleris36* Haahr42* Benjamin37* Nongonierma54* Roberts68* Roberts38*; Roberts68* Weel43* Heilig15; Roberts68*
from http://www.thegoodscentscompany.com/; * most likely CAS-no was assigned if not given in the literature.
55
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Page 56 of 66
Table 5: Boundaries for the relationship shown in Fig. 2 – 9 Parameter
Const.
log P
BP
pi0
(-)
(°C)
(hPa)
Water
Protein
Fat
CWR
Thick.
Disacc.
pH
υ
(% w/w)
(% w/w)
(% w/w)
(-)
(% w/w)
(% w/w)
(% w/w)
(%)
(-)
(°C)
80
Ash DWPD
Fig. 2 (A) Upper limit
n.a.
+4.45
269
1199.7
100.0
24.4
14.8
100.0
4.01
35.0
4.11
99
7.00
Lower limit
n.a.
-1.34
-2
0.00
62.2
0.0
0.0
0.0
0.00
0.0
0.00
3
3.50
4
P-Value
0.454
0.000
0.805
0.000
0.900
0.480
0.232
0.686
0.290
0.358
0.905
0.510
0.027
0.000
Upper limit
n.a.
+4.45
267
1199.7
100.0
24.4
14.8
100.0
4.01
35.0
4.11
99
7.00
80
Lower limit
n.a.
-1.34
-2
0.01
62.6
0.0
0.0
0.0
0.00
0.0
0.00
3
3.50
4
P-Value
0.251
0.000
0.035
0.000
0.997
0.544
0.084
0.476
0.134
0.835
0.201
0.409
0.035
0.000
Upper limit
n.a.
+3.90
267
1199.7
100.0
24.4
14.8
100.0
4.01
35.0
4.11
99
7.00
80
Lower limit
n.a.
-0.34
20
0.01
62.6
0.0
0.0
0.0
0.00
0.0
0.00
3
3.50
4
P-Value
0.024
0.000
0.000
0.000
0.136
0.816
0.021
0.843
0.084
0.858
0.213
0.418
0.211
0.000
Upper limit
n.a.
+3.90
267
1199.7
100.0
n.a.
n.a.
n.a.
2.20
35.0
0.62
n.a.
7.00
80
Lower limit
n.a.
-0.34
20
0.01
62.6
n.a.
n.a.
n.a.
0.00
0.0
0.00
n.a.
3.50
7
P-Value
0.094
0.000
0.000
0.669
0.095
n.a.
n.a.
n.a.
0.144
0.095
0.117
n.a.
0.161
0.000
Upper limit
n.a.
+3.39
214
121.1
91.2
12.0
n.a.
100.0
4.01
17.7
3.14
99
7.00
40
Lower limit
n.a.
+0.71
73
0.05
80.1
3.2
n.a.
0.0
0.00
4.3
0.49
3
4.00
8
P-Value
0.626
0.000
0.000
0.036
0.914
0.954
n.a.
0.979
0.920
0.743
0.811
0.709
0.091
0.253
Upper limit
n.a.
+4.09
269
150.0
89.6
24.4
14.8
100.0
4.01
33.4
4.11
99
7.00
40
Lower limit
n.a.
+0.71
77
0.00
63.0
3.0
0.1
2.0
0.00
4.5
0.67
3
4.00
4
P-Value
0.149
0.000
0.000
0.147
0.093
0.476
0.108
0.201
0.321
0.476
0.674
0.925
0.530
0.414
Upper limit
n.a.
n.a.
n.a.
n.a.
100.0
12.0
4.0
100.0
2.20
35.0
1.51
99
7.00
40
Lower limit
n.a.
n.a.
n.a.
n.a.
62.6
0.0
0.0
0.0
0.00
0.0
0.00
3
4.00
4
P-Value
0.000
n.a.
n.a.
n.a.
0.000
0.000
0.000
0.499
0.417
0.000
0.000
0.936
0.007
0.000
Upper limit
n.a.
n.a.
n.a.
n.a.
100.0
12.0
12.0
100.0
2.20
35.0
1,51
99
7.00
40
Lower limit
n.a.
n.a.
n.a.
n.a.
62.6
0.0
0.0
0.0
0.00
0.0
0.00
3
4.23
4
P-Value
0.307
n.a.
n.a.
n.a.
0.292
0.321
0.418
0.885
0.921
0.877
0.269
0.302
0.137
0.669
Upper limit
n.a.
n.a.
n.a.
n.a.
100.0
12.0
12.0
100.0
4.01
35.0
3.14
99
7.00
40
Lower limit
n.a.
n.a.
n.a.
n.a.
63.1
0.0
0.0
0.0
0.00
0.0
0.00
3
4.00
4
P-Value
0.000
n.a.
n.a.
n.a.
0.002
0.041
0.317
0.974
0.003
0.269
0.206
0.496
0.821
0.044
Fig. 2 (B)
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
n.a. = not applicable.
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Fig. 1 100000
5
BB
A 4
10000
1000
2
KMG (-)
log P (-)
3
1
100
0
10 -1 -2
1
1
Investigated aroma compounds
56
1
57
Experimentally determined KMG-values
ACS Paragon Plus Environment
535
Journal of Agricultural and Food Chemistry
Page 58 of 66
Fig. 2
12500
5000
Observed KMG (-)
A
B
7500
3000
2500
1000
-1000
-2500 -2500
2500
7500
12500
-1000
Predicted KMG (-)
58
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1000
3000
5000
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Journal of Agricultural and Food Chemistry
Fig. 3
Observed KMG (-)
2500
1500
500
-500 -500
500
1500
2500
Predicted KMG (-)
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Fig. 4
1000
5000
B
Observed KMG (-)
A 750 3000 500
250 1000 0
-1000
-250 -1000
1000
3000
5000
-250
Predicted KMG (-)
60
ACS Paragon Plus Environment
0
250
500
750
1000
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Journal of Agricultural and Food Chemistry
Fig. 5
7500
1000
B
A Observed KMG (-)
750 5000 500 2500 250 0
0
-2500 -2500
-250 0
2500
5000
7500
-250
Predicted KMG (-)
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0
250
500
750
1000
Journal of Agricultural and Food Chemistry
Page 62 of 66
Fig. 6
10000
10000
Observed KMG (-)
AA
B B
6000
6000
2000
2000
-2000
-2000 -2000
2000
6000
10000
-2000
Predicted KMG (-)
62
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2000
6000
10000
Page 63 of 66
Journal of Agricultural and Food Chemistry
Fig. 7
7500
Observed KMG (-)
5000
2500
0
-2500 -2500
0
2500
5000
7500
Predicted KMG (-)
63
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
Fig. 8
Observed KMG (-)
2500
1500
500
-500 -500
500
1500 Predicted KMG (-)
64
ACS Paragon Plus Environment
2500
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Page 65 of 66
Journal of Agricultural and Food Chemistry
Fig. 9
Observed KMG (-)
2500
1500
500
-500 -500
500
1500
2500
Predicted KMG (-)
65
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TOC
66
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Page 66 of 66